A Framework for Establishing a Semantic Web in a ...

1 downloads 30774 Views 3MB Size Report
websites represent the biggest global database that lacks the existence of a proper ..... for similarity measurement between ontologies [9]. various software development .... Number of Backlinks Number of ... Majestic Seo Referring Domains.

A Framework for Establishing a Semantic Web in a University Web Site: A Case Study University of Gezira

Gais Alhadi Babikir Alhadi

B.Sc. (Honors) in Mathematical \Computer, University of Gezira (2011) A Dissertation Submitted to the University of Gezira in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science in Computer Science Department of Computer Science Faculty of Mathematical and Computer Sciences University of Gezira

2014 November

A Framework for Establishing a Semantic Web in a University Web Site: A Case Study University of Gezira

Gais Alhadi Babikir Alhadi

Supervision Committee: Name

Position

Signature

Dr. Murtada Khalafallah Elbashir

Chair Person

……………………

Dr. Abd Allah Akoode Osman

Co-supervisor

……………………

Date: November 2014

i

A Framework for Establishing a Semantic Web in a University Web Site: A Case Study University of Gezira

Gais Alhadi Babikir Alhadi

Examination Committee: Name

Position

Dr. Murtada Khalafallah Elbashir

Chair Person

Dr. Yasir Abdelgadir Mohamed

External Examiner

Dr. Mohammed Abbas Alameen

Internal Examiner

Date of Examination: 15/11/2014

ii

Signature …………………… ....………………… ……………………

Declaration I declare that this thesis entitled: " A Framework for Establishing a Semantic Web in a University Web Site: A Case Study University of Gezira" is the result of my own research expected as cited in references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

Signature:…………………. Name:………….…………. Date:………………...…….

iii

DEDICATION To My parent To My Brothers To My Friends To Classmates (My batch)

iv

ACKNOWLEDGEMENT First, thanks to the supervisor Dr. Murtada Khalafalla Albasher for his advice and He Did not Stingy any information for me ,Thanks to Co.supervisor Dr. Abd Allah Akood and Dr. Mohammed Abbas Alameen for her help . Also I thank Uztaz. Anas Esa Abdalkareem very much for his assistance and support. Also I will never forget my brother and my teacher Dr. Mohammed Albarra Hassan

v

A Framework for Establishing a Semantic Web in a University Web Site: A Case Study University of Gezira Gais Alhadi Babikir Alhadi Master of Science in Computer Science 15/11/2014 Department of Computer Science Faculty of Mathematical and Computer Sciences University of Gezira Abstract The advent of the World Wide Web (WWW) technology has yielded an escalating demand for managing data, information and knowledge effectively. Recently, various research groups started delivering results from their semantics based on search engines, however, most of them are in their initial stages. Apparently, the current websites represent the biggest global database that lacks the existence of a proper semantic structure. Therefore, it makes it difficult for the machine to understand the information provided by the users. Unfortunately, this shortcoming is inherited in all universities web sites. Indeed, some of these websites place the information incorrectly, untidy and incomplete, which leads to a delay in ranking the university. University of Gezira is one of the universities websites that has this problem. University website ranking plays a key role for quality assessment in terms of usability and visibility factors. The aim of this thesis is to design a framework for establishing a semantic web in a university web site in general and taking University of Gezira as a particular case. This can be accomplished by organizing the information in conceptual spaces according to its meaning by using Semantic Web and ontology. The main objective of applying the concept of the semantic web is to enhance the ranking of the university. In this research, Microsoft Visual Studio 2010 is used to design the web pages, and SQL server 2008 is used to host the database. Protégé and Microsoft Visio are used as tools for the ontology. The website that is designed according to web 3.0 standards will have clear presentation of information and the information itself will be current. We expect that the usability of the website will increase to it is maximum level, and the site organization will be enhanced to the highest level. Also the following metrics can be enhanced to their maximum level: contents, compatibility, using file naming rules, using folder naming rules. Thus the ranking of the University website will be enhanced.

vi

‫إطار عمل لتأسيس الويب الداللي في موقع الجامعة ‪ :‬دراسة حالة جامعة الجزيرة‬ ‫قيس الهادي بابكر الهادي‬ ‫ماجستير العلوم في علوم الحاسوب ‪15/11/2014‬‬ ‫قسم علوم الحاسوب‬ ‫كلية العلوم الرياضية والحاسوب‬ ‫جامعة الجزيرة‬ ‫ملخص الدراسة‬ ‫أدى اختراع الشبكة العنكبوتية العالمية )‪ (World Wide Web‬الى الطلب المتصاعد إلدارة البيانات والمعلومات‬ ‫والمعرفة على نحو فعال‪ .‬في اآلونة االخيرة‪ ،‬بدأت مجموعات بحثية مختلفة بتقديم النتائج من خالل دالالت على‬ ‫أساس محركات البحث‪ ،‬ولكن معظمها في مراحلها األولية‪ .‬مواقع الويب الحالية تمثل أكبر قاعدة بيانات عالمية تفتقر‬ ‫إلى وجود بنية داللية )‪ (Semantic Structure‬مناسبة‪ ،‬مما يجعل من الصعب على اآللة فهم المعلومات المقدمة‬ ‫من قبل المستخدمين‪ .‬لسوء الحظ‪ ،‬هذا القصور متوارث في جميع مواقع الجامعات على شبكة اإلنترنت‪ .‬في الحقيقة ‪،‬‬ ‫بعض المواقع وضعت المعلومات بشكل غير صحيح ‪ ،‬غير مرتب وغير مكتمل‪ ،‬مما يؤدي إلى تأخير الجامعة في‬ ‫التصنيف العالمي‪ .‬جامعة الجزيرة هي واحدة من الجامعات التي لديها هذه المشكلة‪ .‬حيث انه من المعلوم ان موقع‬ ‫الجامعة يلعب دورا رئيسيا في تقويم الجودة من حيث سهولة االستخدام ووضوح الرؤية‪ .‬تهدف هذه األطروحة إلى‬ ‫وضع إطار عمل لتأسيس الويب الداللي في موقع الجامعة بشكل عام وجامعة الجزيرة كحالة دراسة‬ ‫( ‪a Framework for Establishing a Semantic Web in a University Web Site : A Case Study‬‬ ‫‪ .)University of Gezira‬و يمكن تحقيق ذلك من خالل تنظيم المعلومات في المساحات المناسبة وفقا لمعناها عن‬ ‫طريق استخدام الويب الداللي واألنطولوجيا )‪ .(Semantic Web and Ontology‬الهدف الرئيسي من تطبيق‬ ‫مفهوم الويب الداللي هو تحسين ترتيب موقع الجامعة االلكتروني‪ .‬في هذا البحث تم استخدام مايكروسوفت فيجوال‬ ‫ستديو‪ )Microsoft Visual Studio 2010( 2010‬لتصميم صفحات الويب‪ ،‬و أس كيو إل سيرفر ‪SQL ( 2008‬‬ ‫‪ )server 2008‬الستضافة قاعدة البيانات‪ .‬كما تم استخدام بروتيج ‪ (Protégé_4.0.2) 4.0.2‬ومايكروسوفت‬ ‫فيزيو (‪ (Microsoft Visio 2010‬كأدوات لتطبيق األنطولوجيا (‪.)Ontology‬هذا الموقع الذي تم تصميمه وفقا‬ ‫لمعايير الويب ‪ )Web 3.0) 3.0‬سيكون اكثر انتشارا‪ ،‬و سيتم عرض المعلومات بشكل واضح وسريع‪ ،‬مع امكانية‬ ‫التحديث بسهولة ‪.‬كما أن قابلية استخدام الموقع ستزيد للحد األقصى‪ ،‬وسيتم تحسين تنظيم الموقع إلى أعلى مستوى‪.‬‬ ‫كذلك تم تعزيز المقاييس التالية الى أقصى مستوى لها‪ :‬المحتويات‪ ،‬والتوافق‪ ،‬وذلك باستخدام قواعد تسمية الملفات‪،‬‬ ‫وقواعد تسمية المجلدات‪ .‬وبالتالي سيحسن ترتيب موقع الجامعة االلكتروني بشكل كبير‪.‬‬

‫‪vii‬‬

Table of Contents Contents Signatures of the supervision committee members Signatures of the examination committee members Declaration Dedication Acknowledgement Abstract ‫ملخص الدراسة‬ Table of Contents List of Table List of Figures List of Acronyms Chapter One 1.1Introduction 1.2 The Research Problem 1.3 The Research Objectives 1.4 Structure of the Research Chapter Two 2.1 Literature Review Chapter Three 3.1 University of Gezira History and Development 3.2 Criteria for Evaluating Web Resources 3.3 Universities Ranking 3.4 Information on prominent International Rankings and the position of the order of the University of Gezira September 2014 3.4.1 Webometrics 3.4.2 4icu 3.4.3 QS 3.4.4 Scimago 3.4.5 Times Higher Education (THE) 3.4.6 Academic Ranking of World Universities (ARWU) 3.5 Ranking and Semantic Web 3.6 Semantic Web 3.7 Sceptical Reactions 3.8 Reasons for the Semantic Web 3.9 Semantic Web Technologies in use 3.10 URI and Unicode 3.11 RDF and rdfschema 3.12 Ontology 3.13 Ontology Representation 3.14 Ontology Applications 3.15 How are ontologies different from relational databases? 3.16 How are ontologies different from object-oriented modeling? 3.17 Relational Databases into the Semantic Web viii

Page i ii iii iv v vi vii ix x xi xi 1 2 2 3 4 8 9 10 11 12 12 13 13 13 14 15 16 16 17 17 19 20 21 22 23 24 25 26

3.18 Motivation and Benefits 3.19 Semantic annotation of dynamic web pages 3.20 Heterogeneous database integration 3.21 Ontology-based data access 3.22 Mass generation of Semantic Web data 3.23 Ontology learning 3.24 Definition of the intended meaning of a relational Schema 3.25 Integration of database content with other data sources 3.26 Classification of Approaches 3.27 Tool Structure Chapter Four 4.1 Research Methodology 4.2 Methodologies for Development of Semantic Web Based Systems 4.2.1 Analysis Phase 4.2.2 Design Phase Chapter Five 5.1 Creating Website Information Architecture and Content 5.2 Create Information Architecture 5.2.1 Defining key stakeholders 5.2.2 .Identifying user's goals and expectations 5.2.3 Defining site’s content areas 5.2.4 Organizing the content areas 5.2.5 Creating the site map 5.2.6 Outlining the navigational structure 5.2.7 Labeling the content areas 5.2.8 Creating wireframes 5.3 Information architecture standards 5.3.1 Naming conventions 5.3.2 Website title 5.4 Design Guidelines 5.4.1 Clearly identify university on every page 5.4.2 Use images that reflect your university’s values and priorities 5.4.3 Make about university page 5.4.4 Highlight strengths and achievements 5.4.5 Make it easy for visitors to view a list of majors and academic programs 5.4.6 Provide information about job placement after graduation, and link to it from the alumni section of the website 5.4.7 Prepared visitors to search for information about university on external Websites 5.5 Improve the presentation of data 5.6 Tools for Ontology Development 5.7 Ontology Development for University of Gezira 5.8 Vocabularies 5.9 RDF and RDF Schema Chapter Six ix

28 28 29 29 30 31 31 32 32 34 35 35 36 40 42 44 44 44 44 45 45 46 48 49 49 49 49 50 50 51 51 52 53 54 55 56 56 56 60 61

6.1 Results and Discussions 6.2 Conclusion 6.3 Recommendation References

65 69 69 70

List of Figures Figure

Page

3.1 An abstract version of the Semantic Web Layer [20] 3.2 Language Layers on the Web 3.3 Ontology in service over the internet 3.4 Classification of Approaches 3.5 The classification criteria and descriptive parameters used 4.1 Activities of analysis phase 4.2 The architecture of proposed model 4.3 Creating the site map 4.4 Wireframe of University of Gezira 4.5 Ontology for classes (Faculties, Departments, Staff, Students, Persons , Programs, Subjects and Grade points) 4.6 Ontology for all students operation in University of Gezira website 4.7 Taxonomy of Persons within the University of Gezira website 4.8 OWL visualization of sub-classes "Staff" in the person ontology within the University of Gezira website 4.9 A simple RDF graph 5.1 Site map of University of Gezira website 5.2 Wireframe of University of Gezira old website 5.3 Site map of University of Gezira old website 5.4 Illustrate Children and Parent page in site map of University of Gezira 5.5 Sketch of a navigation scheme created from a site map of University of Gezira 5.6 Main menu and quick menu in University of Gezira 5.7 Example of a related links section 5.8 Example of consistent naming conventions 5.9 Use images in main page for University of Gezira website 5.10 Illustrates About University of Gezira 5.11 Strengths and achievements in University of Gezira 5.12 Illustrates information about alumni and job placement in University of Gezira 5.13 External sites in University of Gezira website 5.14 (A): General Layout of University of Gezira Ontology 5.14 (B): Left Layout of University of Gezira Ontology 5.14 (C): Right Layout of University of Gezira Ontology 5.15 Implementation of University of Gezira Ontology in Protégé_4.0.2 5.16 Protégé Class hierarchy of person ontology within the University of Gezira website

5.17 Uses of person ontology within the University of Gezira website 5.18 RDF/XML rendering: for persons within the University of Gezira website 5.19 OWL Functional syntax rendering: for persons within the University of Gezira website x

18 20 24 33 33 36 36 37 38 39 39 40 41 41 42 43 43 45 46 47 48 50 51 52 53 54 55 56 57 58 59 60 61 63 64

List of Tables Tables 6.1 Expected Criteria for Evaluating Web Resources

List of Acronyms WWW SW WSDL OAI-PMH OWL ARWU HTML URI IRI XML XHTML RDF SPARQL OBDA SQL CMS SMIL AI SWOL

World Wide Web Semantic Web Web Service Description Language Open Archives Initiative Protocol for Metadata Harvesting Web Ontology Language Academic Ranking of World Universities Hypertext Markup Language Uniform Resource Identifier Internationalized Resource Identifier Extensible Markup Language Extensible Hypertext Markup Language Resource Description Framework Simple Protocol and RDF Query Language Ontology-Based Data Access Structured Query Language Content Management Systems Synchronized Multimedia Integration Language Information Architecture Semantic Web Ontology Language

xi

Page 68

Chapter One 1.1 Introduction The advent of the World Wide Web (WWW) in the mid-1990s has resulted in an escalating demand for managing data, information and knowledge effectively. There is now so much data on the web that managing it with conventional tools is becoming almost impossible. New tools and techniques are needed to effectively manage this data. Therefore, to provide interoperability as well as warehousing between the multiple data sources and systems, and to extract information from the databases and warehouses on the web, various tools are being developed. Consequently the web is evolving into what is now called the Semantic Web [1]. A website is a collection of web pages, typically common to a particular domain name or sub domain on the World Wide Web on the Internet .The web is becoming the most important scholarly communication tool and it makes more scientific information accessible easy. In recent years, university Web rankings have become very importance around the world. The central hypothesis of the ranking is that the university’s web presence reflects its global performance, the quality of its departments and services, the impact of its outputs and its international prestige. One of the most important dimensions in university Website ranking is visibility factor and quality of presentation. The dimension includes qualitative and quantitative criteria. Presentation and visibility of university website is one of the most important dimensions of the web quality. This field is a common issue in different Websites apart from their application and usage. More attention should be placed on the visual appearance of the website, in terms of a more usable layout and pleasant graphics, due to their role on perceived online service and quality [2, 3]. However when evaluate a Websites dimensions such as data quality, visibility and quality of presentation must be considered [4]. Also we have to keep in mind that some of the criteria are depend to context of the Website and priorities and influence coefficient may be changed. This research attempts to gain priorities in measuring quality of University of Gezira website and evaluated the website by some quantitative criteria and using Semantic Web.

1

The Semantic Web is an extension of the current World Wide Web, not a separate set of new and distinct websites. It builds on the current World Wide Web constructs and topology, but adds further capabilities by defining machine processable data and relationship standards along with richer semantic associations. Existing sites may use these constructs to describe information within web pages in ways more readily accessible by outside processes such as search engines, spider searching technology, and parsing scripts. The emergence of the Semantic Web is a natural progression in accredited

information

theories,

borrowing

concepts

from

the

knowledge

representation and knowledge management worlds as well as from revised thinking within the World Wide Web community [5].

1.2 The Research Problem Current websites represent the biggest global database, which lacks the existence of a semantic structure, and hence, it makes it difficult for the machine to understand the information provided by the user. This fact of the current websites is inherited on all universities web sites. Some of these websites place the information incorrectly, untidy and incomplete, which affect the ranking of the university. University of Gezira website is suffering from this problem.

1.3 The Research Objectives This thesis is aimed to: 

Upgrade the University of Gezira Websites to Web3.0 standards structure by using Semantic Web and ontology.



Enhance the ranking of the University of Gezira Website by applying Web3.0 standards and the universities website standards by organizing the information in conceptual spaces according to its meaning by using Semantic Web and ontology.

2

1.4 Structure of the Research This research consists of six chapters, chapter one presents introduction, problem identification, objectives, and structure of the research. Chapter two presents the literature review and the related studies. Chapter three shows the University of Gezira History and Development, Criteria for Evaluating Web Resources, Universities Ranking, Semantic Web and Ontology. In chapter four, the methodology for upgrading the design of the University of Gezira website according to the international universities web standards is presented. Chapter five presents the design and implementation for the University of Gezira website, finally chapter six presents the conclusion and recommendations.

3

Chapter Two 2.1 Literature Review In 2003 by Yiqiao Wang. Entitled Information Retrieval and Semantic Structure Matching for Assessing Web-Service Similarity [6]. The plethora of web services available on the World Wide Web presents a great opportunity to users. It also gives rises to a great challenge: to enable discovery, reuse and interoperation of web services and software components on the web. To support programmatic service discovery, in this study it have developed a suite of methods that utilize both the semantics of natural language descriptions and identifiers in WSDL (Web Service Description Language) descriptions and the structures of their operations, messages and types to assess the similarity of two WSDL services. These service discovery methods that combine semantic and structure similarity assessments enable a substantially more precise service-discovery process . In September 2004 by Albert Strasser. Entitled A Semantic Web Approach to Implementing an OAI Data Provider Using BIBTEX Bibliographic Data [7]. Although, offering Web-based portals to such repositories of bibliographic data helped Digital Libraries to become a great success, there exist still a number of problems related to the management of this kind of metadata. Different Digital Libraries manage bibliographic data indifferent formats so that integrated solutions for searching publications in multiple libraries are still not possible. Actually, users have to visit a number of Digital Library Web portals to find the needed publications. Technically, utilizing different formats does not allow bibliographic data to be exchanged between different Digital Libraries. To enable the exchange of bibliographic data it is necessary to agree on standardized formats. A “defacto” standard for the exchange of bibliographic data in the Web is the combination of the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and Dublin Core. In many cases the simple Dublin Core standard might not be powerful enough to include all the information available for publications. Many Digital Libraries rely on other, more expressive, bibliographic formats.

4

In JUNE 2005 GÜLER KALEM. Entitled SEMANTIC WEB APPLICATION: ONTOLOGY-DRIVEN RECIPE QUERYING [8].Its main purpose of the study to investigate and research the Semantic Web concept and get a solid understanding of the concepts together with its difficulties, problems and the ability to be used in real world applications. In developing the Semantic Web application, the following practical problems arise: •

To process data defined with the Semantic Web language OWL (Web Ontology Language).



To execute queries on OWL ontologies.



To use meaning when applied within applications.



To combine and process different information located at different systems, on a single system.

The implementation part of the thesis mostly deals with the problems mentioned in the above list. As a domain a Web-based application dealing with food recipes has been chosen. Instead of building all the application logic into static standard HTML with a scripting language, all the information and application logic have been moved into an OWL ontology file. All the data and the application logic should reside in the OWL Web ontology as much as possible for a more effective system. The application is able to respond meaningfully no matter how the queries are constructed. In 2010 by Farooq, A., Arshad, M. J., & Shah, A. Entitled A layered approach for similarity measurement between ontologies [9]. various software development methodologies have been proposed for the development of software applications for different domains. The main objectives of those methodologies were to meet user’s requirements, find out means to suggest a systematic software development and reduce the maintenance cost of the developed software. On the emergence of the Web and to develop the web-based software systems, some existing methodologies have been extended. Also, new approaches (or informal methodologies) are introduced for the development of web-based systems because the development process for these systems is not considered as an extension of the classical software engineering, although both development processes for web-based systems and non-web-based systems have the 5

same basic objective which is software development. Of course, the development of the web-based systems needs a new kind of development methodologies which should meet and capture their unique and different requirements. Currently available software development methodologies are inappropriate and unsuitable to use for the development of web-based software systems, especially for the third generation web, called Semantic Web. They proposed a model for the development of semantic web systems.

This

model

can

be

used

as

a

benchmark

to

propose

formal

methodologies for the development of the semantic web systems. In 2011 by Kargar, M. J. Entitled University Website Ranking; A Case Study in IRAN [10]. Web quality is not a new field in information technology but there is a lack of academic research which focuses on university web quality. Moreover, far too little attention has been paid to usability problem in university web quality. For evaluating usability problem the research was classified web and information quality, because these areas influence the web usability. In 2011 by Muhammad Naeem and Omar Tariq. Entitled RDFa as Semantic Markup and Web Visibility [11] . Web visibility is the appearance of web sites in search engines. Web visibility in search engine is an important factor to improve the ecommerce on the web. If the web site gets high ranking in search engines it will attract more web traffic. Semantic markup is a technique to structure a web site, so it can be understandable by humans and computers. This allows the crawler or spider to understand the content of the web site during the search engine process. Semantically structured web sites increase the web visibility in search engines. RDFa is a semantic markup and supported by the W3C. This study focused on the RDFa as a semantic markup technique. And it’s shows two aspects of RDFa i.e. what are the benefits and barriers of using RDFa in structuring and enhancing the web visibility of web sites in search engines, and how web developers implement RDFa. First result of this study shows the benefits and barriers of using RDFa according to the web developers. Second result is a guideline for helping the companies that are planning to implement the RDFa in structuring their web sites.

6

In 2012 by Hadeel S. AL-Obaidy and Amani Al. Heela. Entitled An Approach for Building Semantic Web Library [12]. Its examines the annotation processes which represent one of the most important elements used in the development of a Semantic Web service for e-library, as well as also to enrich ontology with knowledge. Similar to any process, annotation has inputs and outputs. The inputs are the documents and ontology, whereas the outputs represent an RDF document to be used by any Semantic Web Service. Future research is needed to shade some light on the potential use of semantic annotation technology. There are several areas of research need to be undertaken to investigate the use of ontology such as developing and testing phase for the Semantic Web service for e-library, enhancing the proposed system and adding more functionality to e-library, extending the use of Semantic Web to other services in e-library, Maximizing the benefits of Semantic Web by reusing it for presenting

other

type

of knowledge, expand the use of library Semantic Web

application to the mobile technology and develop a web application that working in WAP . In 2013 by Meymandpour, R., & Davis, J. G. Entitled Ranking Universities Using Linked Open Data. In LDOW [13]. Its propose a novel approach to take advantage of structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai

Jiao Tong University,

Times Higher Education, and QS.

Presented an innovative ranking metric that takes into account the in formativeness of entities on the Web of Data. By computing the quality of facts available publicly in Linked Open Data, and measured the relative footprint of world universities on the Web, and also highlight the need for a Linked Open Data providing university- and researchrelated semantics. As a structured and reliable source of semantic data. And focus more on the accuracy of the ranking by capturing more semantics from LOD cloud and by eliminating any trace of redundancy.

7

Chapter Three 3.1 University of Gezira History and Development: The First batch of students were registered for their first semester in September 1978 and graduated in 1984. There were four faculties at that time: Agricultural Sciences, Economics and Rural development, Medicine and Science and Technology (Presently Engineering and Technology). The Total number of students graduated from the University in the batch was only 172. Now the University comprises Seventeen faculties spreading all over the Gezira state, in addition to the community college. The number of research institutes in various scientific and humanitarian disciplines has risen to nine, specialized research and training centers were established in faculties and institutes. The University of Gezira is currently spread in nine campuses in the Gezira state and one campus in the northern state. Campuses in the city of Wad Medani are the University City in Neshaishiba, Elrazi Campus and Hantoub Campus. The University City in Neshaishiba is located in the northern outskirt of the city of Wad Medani, and includes the university administration, central deanships, Faculty of Agricultural Sciences, Engineering and Technology, Economics and Rural Development, Textiles, and the National Institute for Development of Horticultural Exports, the Sugar Institute and the University of Gezira Farm. Elrazi campus, is located in the southern part of the city of Wad Medani and includes Faculties of Medicine, Pharmacy, Applied Medical Sciences, Medical Laboratory Science, Dentistry, and Mathematical and Computer Sciences, and the Water Management and Irrigation Institute, The National Cancer Institute and the National Oilseeds Processing and Research Institute, in addition to the Education Promotion Center, University Press and University of Gezira Consulting House. The campus in Hantoub houses the Faculty of Education - Hantoub and the Institute of Islamization of Knowledge. There are six other University campuses in different locations in the Gezira state, including the Faculty of Educational Sciences which is located in Elkamlin town, the Faculty of Education – El Hassahissa located in El Hassahisa town, the Faculty of Animal Production located in Elmanagil, the Faculty of Health and Environmental Sciences located in Elhush in the South Gezira Locality and the Faculty of Developmental Studies located in Umm Elgora Locality.. At the Locality of Wad Medani in addition to the city of Wad Medani campuses there is the 8

Faculty of Communication Sciences in Fadasi (Wad Medani suburb). And at Eldaba town (Northern state) there is the National Institute for Desert studies [14].

3.2 Criteria for Evaluating Web Resources There are many criteria, such as those below, to gather evidence on the quality of the information in the Web site. These criteria help us to Evaluating Web Resources, which helps to improve the assessment of the site globally. 1. Authority: Who created the site? 

What is their authority? 

Do they have expertise or experience with the topic?



What are their credentials, institutional affiliation?



Is organizational information provided?



Does the URL suggest a reputable affiliation with regard to the topic-personal or official site; type of Internet domain. (i.e., .edu: educational institution)

2. Objectivity: Is the purpose and intention of the site clear, including any bias or particular viewpoint? 

Are the purpose and scope stated?



Who is the intended audience?



Is the information clearly presented as being factual or opinion, primary or secondary in origin?



What criteria are used for inclusion of the information?



Is any sponsorship or underwriting fully disclosed?

3. Accuracy: Is the information presented accurate? 

Are the facts documented or well-researched?



Are the facts similar to those reported in related print or other online sources?



Are the Web resources for which links are provided quality sites?

4. Currency: Is the information current? 

Is the content current?



Are the pages date-stamped with last update?

5. Usability: Is the site well-designed and stable? 9



Is the site organization logical and easy to manuever?



Is the content written at a level that is readable by the intended audience?



Has attention been paid to presenting the information as error-free (e.g., spelling, punctuation) as possible?



Is there a readily identifiable link back to the institutional or organizational home page?



Is the site reliably accessible? [15]

3.3 Universities’ Ranking Every day millions of people visit university web portals looking for information. This could be, for example, students looking for course information, change in lecture times, laboratory tables, account access or teacher contact information. It is very important that whatever it is the user is searching for is easy to find and the content is easily understood. Importance of university Website opened a new field in Web evaluation studies. The rankings went truly international in 2003 when Shanghai Jiao Tong University published the results of the first global university ranking. The importance of rankings seems, since then, to have grown exponentially [16]. Webometric is a new term which was launched in 2004. It is an initiative of the cybermetrics lab, a research group of the Centro de Ciencias Humanas y Sociales (CCHS), which is part of the National Research Council of Spain. The Webometrics ranking of world universities based on a composite indicator that takes into account both the volume of the Web contents (number of web pages and files) and the visibility and impact of these web publications according to the number of external in links [10]. This ranking system measures how strongly a university is present in the web by its own web domain, sub-pages, rich files, scholarly articles etc. The central hypothesis of this approach is that web presence is a reliable indicator of the global performance and prestige of the universities and as such, is an indirect way to measure all the university missions (teaching, research, transfer).

10

3.4 Information on prominent International Rankings and the position of the order of the University of Gezira September 2014 Ranking Schema

University of Gezira World Rank

QS World Rankings

N/A

4icu

5348

Webometrics

7557

Scimago

N/A

Times Higher Education

N/A

Shanghai Ranking

N/A

3.4.1 Webometrics Profile: www.webometrics.info. Webometrics is an emerging discipline out of the growth of WWW and publication of the scientific research using the WWW as a vehicle for disseminating, propagating and publishing by the individuals and organizations. Webometrics data has been used to rank the world universities on the web serving as indicators of their academic performance [17]. Since 2004, the Ranking Web (or Webometrics Ranking) is published twice a year (data is collected during the first weeks of January and July for being public at the end of both months), covering more than 20,000 Higher Education Institutions worldwide. Methodology Indicator Impact

Meaning

Source

Weight

Number of Backlinks Number of

Majestic

Backdomain

SEO ahrefs

Presence

Number of webpages

Google

20%

Openness

Number of papers .pdf, .doc,

Google

15%

.docx,.ppt

Scholar

Number of paper in the 10% top cited

Scimago

Excellence

50%

15%

PRESENCE (20%). The total number of webpages hosted in the main web domain (including all the subdomains and directories) of the university as indexed by the largest commercial search engine (Google). It counts every webpage, including all the formats recognized individually by Google, both static and dynamic pages and other rich files.

11

IMPACT (50%). The quality of the contents is evaluated through a "virtual referendum", counting all the external in links that the University web domain receives from third parties. Those links are recognizing the institutional prestige, the academic performance, the value of the information, and the usefulness of the services as introduced in the webpages according to the criteria of millions of web editors from all over the world. OPENNESS (15%). The global effort to set up institutional research repositories is explicitly recognized in this indicator that takes into account the number of rich files (pdf, doc, docx, ppt) published in dedicated websites according to the academic search engine Google Scholar. EXCELLENCE (15%). i.e. the university scientific output being part of the 10% most cited papers in their respective scientific fields [18].

3.4.2 4icu Profile: www.4icu.org. 4icu ranks 11,307 colleges and Universities, ranked by web popularity, in 200 countries. The rankings are done twice a year in January and July. Methodology The current ranking is based upon an algorithm including five unbiased and independent web metrics extracted from three different search engines: 

Google Page Rank



Alexa Traffic Rank



Majestic Seo Referring Domains



Majestic Seo Citation Flow



Majestic Seo Trust Flow

Web metrics data are collected on the same day to minimize temporal fluctuations and maximize comparability. A pre-computational filter is adopted to detect outliers in the raw data. Further investigation and a review of Alexa Traffic Rank data is carried out for universities adopting a subdomain (highly not recommended) as their official institutional home page. Once filtered and reviewed, web metrics data are normalized to a scale of 0 to 100 taking into consideration the logarithmic nature in which both the Google Page Rank and the Alexa Traffic Rank are expressed. The three normalized values are aggregated

12

based on a weighted average algorithm which generates the final score and web ranking [19].

3.4.3 QS Profile: www.topuniversities.com. QS Considers about 3500 universities worldwide, evaluates and ranks the top 872. It is published annually in September/October. Methodology Indicator

Source

Weight

Academic

Reputation Global Survey

40%

Employer Reputation

Global Survey

10%

Citations per faculty

SciVerse Scopus

20%

Faculty Student Ratio

20%

International Students

5%

International Faculty

5%

3.4.4 Scimago Profile: www.scimagoir.com.This is a purely research based ranking that means it only measures an institutions research output and its significance and quality of this output. It takes into account 11000 research institutions around the world which includes universities, and public or private research only centers. Methodology It measures the annual research output of an institution i.e. the number of scientist papers published by the university. Additionally it takes into account the percentage of international collaboration, the impact or significance of the institutions research output and also the quality of publications. The quality of the publications is judged by the ratio of the papers that an institution publishes in prestigious international journals. Lastly, this scheme also awards institutions that serve a larger variety of specializations and research topics and institutions whose publications are highly cited [16].

3.4.5 Times Higher Education (THE) Profile: The Times Higher Education World University Rankings (or THE World University Rankings) are annual world university rankings published by the British magazine Times Higher Education (THE) with data supplied by Thomson Reuters that 13

provides citation database information. They include both the overall and the subject rankings. Moreover, the additional World Reputation Rankings which are independent of the main rankings have also been released starting from 2011. Originally, the Times Higher Education began publishing the Times Higher Education– QS World University Rankings in 2004 with Quacquarelli Symonds (QS) but they ended their partnership in 2010 and both started to release their own rankings. QS has published its rankings with the old existing methodology as the QS World University Rankings, while Times created and adopted a new one Today, the Times Higher Education World University Rankings are regarded to be one of the three most influential and widely observed international university rankings, along with the QS World University Rankings and the Academic Ranking of World Universities (ARWU) [20]. Methodology Indicator

Criteria

Weight

Teaching

Learning environment

30%

Research

Volume, income, and reputation

30%

Citations

Research influence

32.5%

Industry income

innovation

2.5%

International Mix

Staff and students

5%

3.4.6 Academic Ranking of World Universities (ARWU) Profile: www.shanghairanking.com. The Academic Ranking of World Universities (ARWU) is first published in June 2003 by the Center for World-Class Universities (CWCU), Graduate School of Education (formerly the Institute of Higher Education) of Shanghai Jiao Tong University, China, and updated on an annual basis. ARWU uses six objective indicators to rank world universities, including the number of alumni and staff winning Nobel Prizes and Fields Medals, number of highly cited researchers selected by Thomson Scientific, number of articles published in journals of Nature and Science, number of articles indexed in Science Citation Index - Expanded and Social Sciences Citation Index, and per capita performance with respect to the size of an institution. More than 1000 universities are actually ranked by ARWU every year and the best 500 are published on the web [21]. 14

Methodology Dimension

Indicator A1. Percentage of incoming students who participated in state mature

Weight 5%

examination A2. Average score of incoming students in state mature examination

5%

A. Teaching and

A3. Percentage of foreign students

5%

Learning (50%)

A4. Academic staff / undergraduate students ratio

4%

A5. Proportion of academic staff with the highest degree

8%

A6. Proportion of academic staff with 1 year or above foreign work

6%

experience A7. Proportion of students with academic scholarships from Ministry of

6%

Education and Science A8. Institutional income per student

2%

A9. Spending on library resources per student

1%

A10. Spending on IT infrastructure and equipment per student

1%

A11. Proportion of undergraduate -level degree recipients who graduated

1%

within regular time A12. Proportion of undergraduate-level degree recipients with 3 months

2%

or above foreign study/practical experience under the state-level agreements

B. Research (36%)

A13. Employment rate of undergraduate-level degree recipients

4%

B1. Total research income per academic staff

4%

B2. Research income from the Ministry of Education and Science per

6%

academic staff

C. Social Service (14%)

B3. Papers published in peer reviewed journals per academic staff

6%

B4. Papers indexed by Web of Science per academic staff

10%

B5. Books published per academic staff

4%

B6. Number of doctorates granted per academic staff

6%

C1. Research income from industry per academic staff

6%

C2. Patents issued per academic staff

8%

3.5 Ranking and Semantic Web Ranking is an important component in most search engines to prioritise search results and to offer the user an immediate list of the most relevant results to a query. As more RDF data is emerging online, a scalable, user-friendly search engine for Semantic Web data is becoming a more pertinent requirement. A Semantic Web 15

search engine enables querying large RDF datasets, which requires ranking functionality to present results to the user in a meaningful way i.e. by prioritizing relevant, important results. That helps improve the assessment of the website.

3.6 Semantic Web The Semantic Web is a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. Is approach develops languages for expressing information in a machine processable form [22].The Semantic Web, described by Tim Berners-Lee et. al. [23], is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. A key difference between the Semantic Web and the present Web lies in the representation of information. In the present Web, the representation is meant for machines to process information at the syntax level. In the future the Semantic Web allows machines to process and reason about information at the semantic level. Simplified, the adoption of the Semantic Web will enable us to store knowledge about web content in a structured form [24]. The Semantic Web, also known as the Web 3.0[25], has effort to enhance current web so that computers can process the information presented on WWW, interpret and connect it, to help humans to find required knowledge. In the same way as WWW is a huge distributed hypertext system, Semantic Web is intended to form a huge distributed knowledge based system. So the new systems are based on a more sophisticated semantic representation of information, to go well beyond the document level, and to understood and processed by machine [26].The focus of Semantic Web is to share data instead of documents. In other words, it is a project that should provide a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.

3.7 Sceptical Reactions An important factor on why the Semantic Web is still not widespread is that a lot of companies and universities do not see the benefit of it [27]. They consider that a distinct keyword declaration coded into their Hypertext Markup Language (HTML) sites 16

is adequate. On the one hand, they do not care about web users who come to their web page searching something.

3.8 Reasons for the Semantic Web The more information we have on the internet the more difficult it is to find terms of specific contexts. Simply searching for keywords is not enough anymore due to incorrect and incomplete keyword declarations and ambiguous words that exist in our natural languages. The signification of the Semantic Web is to bring structure into the information chaos and to stem the information overload so that it becomes possible to find terms of specific contexts [28]. The potential implementation of the Semantic Web is a distinguished online platform for knowledge management in organizations, universities, etc. With the introduction it becomes possible to create, personalize, represent and distribute knowledge over university branches in with respect to their individual needs for display on website.

3.9 Semantic Web Technologies in use In Figure 3.1 we see how the Semantic Web is layered. On the bottom there is the term identification, which is commonly represented by a URI or an Internationalized Resource Identifier (IRI). Thereon, syntax and a structure have to be defined. XML, XMLS and RDF are commonly used technologies. Ontologies are the core concept of Knowledge Representation in the Semantic Web. RDFS and OWL are languages that can produce such models. The logic layer, which enables software agents to reason about the represented data, is composed through ontologies, queries and rules [28].

17

Figure 3.1: An abstract version of the Semantic Web Layer [29] Extensible Markup Language (XML) layer with XML namespace and XML schema definitions makes sure that there is a common syntax used in the Semantic Web. XML is a general purpose markup language for documents containing structured information. A XML document contains elements that can be nested and that may have attributes and content. XML namespaces allow specifying different markup vocabularies in one XML document. XML schema serves for expressing schema of a particular set of XML documents. A core data representation format for Semantic Web is Resource Description Framework (RDF). RDF is a framework for representing information about resources in a graph form. It was primarily intended for representing metadata about WWW resources, such as the title, author, and modification date of a Web page, but it can be used for storing any other data. It is based on triples subject-predicate-object that form graph of data. All data in the Semantic Web use RDF as the primary representation language. The normative syntax for serializing RDF is XML in the RDF/XML form. RDF itself serves as a description of a graph formed by triples. Anyone can define vocabulary of terms used for more detailed description. To allow standardized description of taxonomies and other ontological constructs, a RDF Schema (RDFS) 18

was created together with its formal semantics within RDF. RDFS can be used to describe taxonomies of classes and properties and use them to create lightweight ontologies.

More detailed ontologies can be created with Web Ontology Language

OWL. The OWL is a language derived from description logics, and offers more constructs over RDFS. It is syntactically embedded into RDF, so like RDFS, it provides additional standardized vocabulary. OWL comes in three species - OWL Lite for taxonomies and simple constrains, OWL DL for full description logic support, and OWL full for maximum expressiveness and syntactic freedom of RDF. Since OWL is based on description logic, it is not surprising that a formal semantics is defined for this language. RDFS and OWL have semantics defined and this semantics can be used for reasoning within ontologies and knowledge bases described using these languages. To provide rules beyond the constructs available from these languages, rule languages are being standardized for the Semantic Web as well. Two standards are emerging - RIF and SWRL. For querying RDF data as well as RDFS and OWL ontologies with knowledge bases, a Simple Protocol and RDF Query Language (SPARQL) are available. SPARQL is SQL-like language, but uses RDF triples and resources for both matching part of the query and for returning results of the query. Since both RDFS and OWL are built on RDF, SPARQL can be used for querying ontologies and knowledge bases directly as well. Note that SPARQL is not only query language; it is also a protocol for accessing RDF data [30].

3.10 URI and Unicode The Semantic Web is generally built on syntaxes which use URIs to represent data, usually many triples of URI data that can be held in databases, or interchanged on the World Wide Web using a set of particular syntaxes "called Resource Description Framework" developed especially for the task. supporting the international text style standard [31].

19

Unicode allows

3.11 RDF and RDF schema Resource Description Framework (RDF) is: 

A general metadata format



Used to represent information about Internet resources



Semantic Web extends the expressive capability of the Web



Augments human-readable web pages with machine-processable information

The Semantic Web, with machine processable information contents, will only be possible when further levels of interoperability are established. Standards must be defined not only for the syntactic form of documents, but also for their semantic content. Such semantic interoperability is facilitated by recent W3C standardization efforts, notably XML/XML Schema and RDF/RDF Schema. These efforts are summarized in Figure 2.2 [32].

Figure 3.2: Language Layers on the Web

20

3.12 Ontology Ontologies [33], the term Ontology has been used in several disciplines, from philosophy, to knowledge engineering, where ontology is comprised of concepts, concept properties, relationships between concepts and constraints. Ontologies are defined independently from the actual data and reflect a common understanding of the semantics of the domain of discourse. Ontology is an explicit specification of a representational vocabulary for a domain, definitions of classes, relations, functions, constraints and other objects. Pragmatically, a common ontology defines the vocabulary with which queries and assertions are exchanged among software entities.

Ontologies are used in order to

support interoperability and common understanding between the different parties, are a key component in solving the problem of semantic heterogeneity, thus enabling semantic interoperability between different web applications and services. Ontologies provide a common understanding of a domain that can be communicated between people, and of heterogeneous and widely spread application systems. In fact, they have been developed in Artificial Intelligence (AI) research communities to facilitate knowledge sharing and reuse. The goal of ontology is to achieve a common and shared knowledge that can be transmitted between people and between application systems. Thus, ontologies [34] play an important role in achieving interoperability across organizations and on the Semantic Web, because they aim to capture domain knowledge and their role is to create semantics explicitly in a generic way, providing the basis for agreement within a domain. Ontology is used to enable inter operation between Web applications from different areas or from different views on one area. For that reason, it is necessary to establish mappings among concepts of different ontologies to capture the semantic correspondence between them [35]. However, establishing such a correspondence is not an easy task [36]. The lifecycle of ontologies over the Semantic Web involves different techniques, ranging from manual to automatic building, refinement, merging, mapping, annotation, etc. Each technique involves the specification of core concepts for the population of an ontology, or for its annotation, manipulation, or management [37][38][39][40][41].

21

3.13 Ontology Representation Ontology is comprised of four main components: concepts, instances, relations and axioms. The present research adopts the following definitions of these ontological components: 

A Concept (also known as a class or a term) is an abstract group, set or collection of objects. It is the fundamental element of the domain and usually represents a group or class whose members share common properties. This component is represented in hierarchical graphs, such that it looks similar to object oriented systems. The concept is represented by a “super-class”, representing the higher class or so called “parent class”, and a “subclass” which represents the subordinate or socalled “child class”. For instance, a university could be represented as a class with many subclasses, such as faculties, libraries and employees.



An Instance (also known as an individual) is the “ground-level” component of an ontology which represents a specific object or element of a concept or class. For example, “Sudan” could be an instance of the class “Arab countries” or simply “countries”.



A Relation (also known as a slot) is used to express relationships between two concepts in a given domain. More specifically, it describes the relationship between the first concept, represented in the domain, and the second, represented in the range. For example, “study” could be represented as a relationship between the concept “person” (which is a concept in the domain) and “university” or “college” (which is a concept in the range).



An Axiom is used to impose constraints on the values of classes or instances, so axioms are generally expressed using logic-based languages such as first-order logic; they are used to verify the consistency of the ontology [35].

22

3.14 Ontology Applications Over the years, ontology has become a popular research topic in a range of disciplines, with the aim of increasing understanding of and building a consensus in a given area of knowledge. Ontology also leads to the sharing of knowledge between systems and people. Ontology first appeared in AI laboratories, before being used in other fields, for example: 

Semantic Web: Ontology plays a key role in the Semantic Web in supporting information exchange across distributed environments. The Semantic Web represents data in a machine processable way, which is why it is considered to be an extension of the current Web.



Semantic Web Service Discovery: In the e-business environment, ontology plays an important role by finding the best match for the requester looking for merchandise or something else. It also helps online travel customers obtain a response.



Artificial Intelligence: Ontology has been developed in the AI research community, its goal here being to facilitate the sharing of knowledge and the reuse and enabling of processing between programs, services, agents or organizations across a given domain.



Multi-agent: The importance of ontology in this area is that it provides a shared

understanding

of

domain

knowledge,

allowing

for

easy

communication between agents and thereby reducing misunderstandings. 

Search Engines: These use ontology in the form of thesauri to find the synonyms of search terms, which facilitates internet searching.



E-Commerce: This application uses ontology to facilitate communication between seller and buyer through the description of merchandise, as well as enabling machine-based communication.



Interoperability: The problem of bringing together heterogeneous and distributed systems is known as the “interoperability problem”. In this area, the importance of applying ontology appears explicitly: it is used to integrate different heterogeneous application systems. 23

In the field of services, ontology plays the major role of providing a richer description of these services and terms and the relationships between them in the application domain, leading to a capture of the domain of knowledge in an explicitly representative manner. At the same time, it supports the inference of implied knowledge by declaring the descriptions. The following example is given in order to demonstrate the reasons for considering ontology to be the backbone of the Semantic Web. As mentioned in [36], it illustrates how ontology may be used to match services with semantic meanings. According to this scenario, the service requester invokes a service (for example, request result), which triggers a description of the service request information annotated in metadata. Service providers also describe and advertise their services in metadata to provide answers to the requester, while the service match engine receives the metadata of both provider and requester, upon which it accesses the ontology, which provides a possible identification of result. The service match engine will infer from this whether the request has been satisfied or not [35]. (See Figure 2.3)

Figure 3.3: Ontology in service over the internet

3.15 How are ontologies different from relational databases? Although databases and ontologies have some similarities, they differ in many important features. First of all ontology is not storage for data but is a defining model for the data whereas a relational database is a data repository. Ontology can be used as filter or a framework to access and manipulate data where a database can be used to store the 24

different data instances defined by the ontology. Another important difference is querying. When making queries against a relational database the returned data will be the same data stored previously, just matching some conditions. However when making a query against an ontology, together with some reasoning process, the returned data can be some inferred data which was not stored previously but generated from some facts represented by the ontology. In ontologies, queries can also be made for some specific relations while this is not possible with ordinary relational databases.

3.16 How are ontologies different from object-oriented modeling? Ontology is also different than the object-oriented paradigm even though there are lot things in common, especially when it comes to model real life with class definitions. First of all, the whole concept of ontologies has its theoretical roots in logic. Because of that ontology allows reasoning systems to make automated reasoning on the defined knowledge represented by the ontology. Another important difference is the definition of properties. In ontology, properties are treated as first-class citizens while in the object-oriented paradigm this not true. In the object oriented world, properties are internal to class definitions. In ontology it is possible define multiple inheritance while this is not the case in the object-oriented paradigm. In object-oriented modeling it is only possible to make single inheritance between classes because of overlapping method signatures defined in different super classes when participating in a multiple inheritance relationship. Ontologies allows property inheritance while this not possible with object oriented modeling. While the ontologies allow user defined relations between different classes, object-oriented modeling restricts the relation with the class subclass concept. However, because of the wide acceptance and use of object-oriented modeling and UML, they are accepted as practical specifications when modeling ontologies. But because of the lack of logic capabilities of the object-oriented modeling approach these two different concepts cannot be fully combined and be productive as they are defined today. Currently there is an on-going effort to add logic capability to object-oriented modeling, represented by OCL (Object Constraint Language).

25

3.17 Relational Databases into the Semantic Web Over the last decade and more, the Semantic Web (SW) has grown from an abstract futuristic vision, mainly existing in the head of its inspirer, Tim Berners Lee, into an ever approaching reality of a global web of interlinked data with well-defined meaning. Standard languages and technologies have been proposed and are constantly evolving in order to serve as the building blocks for this “next generation” Web, relevant tools are being developed and gradually reaching maturity, while numerous real world applications already give an early taste of the benefits the Semantic Web is about to bring in various domains, as diverse as life sciences, environmental monitoring, cultural heritage-Government and business process management. This evident progress is the result of years-long research and it comes as no surprise that, nowadays, Semantic Web is perceived as a multidisciplinary research field on its own, combining and gaining expertise from other scientific fields, such as artificial intelligence, information science, algorithm and complexity theory, database theory and computer networks, to name a few. The participation of databases and their role in this evolving Web setting has been investigated from the very beginning of the Semantic Web conception, not only because it was initially compared to a global database, but also because this new at the time research field could take advantage of the great experience and maturity of the database field. However, the collaboration and exchange of ideas between these two fields was not unidirectional: the database community quickly recognized the opportunities arising from a close cooperation with the Semantic Web field and how the latter could offer solutions to long-standing issues and provide inspiration to several database sub communities, interested in heterogeneous database integration and interoperability, distributed architectures, deductive databases, conceptual modeling and so on. Attempts to combine these two different worlds originally focused on the reconciliation of the discrepancies among the two most representative and dominant technologies of each world: relational databases and ontologies. This problem is also known as the database to ontology mapping problem, which is subsumed by the broader objectrelational impedance mismatch problem and is due to the structural differences among relational and object-oriented models. 26

Correspondences between the relational model and the RDF graph model, which is a key component of the Semantic Web, were also investigated and a W3C Working Group has been formed to examine this issue and propose related standards. Nevertheless, the definition of a theoretically sound mapping or transformation between the mentioned models is not an end on its own. The motivation driving the consideration of mappings among relational databases and Semantic Web technologies is multifold, leading to separate problems, where mappings are discovered, defined and used in a different way for each problem case. Originally, database systems were considered by the Semantic Web community as an excellent means for efficient ontology storage, because of their known and well evidenced performance benefits. This consideration has led to the development and production of several database systems, especially optimized for the persistent storage, maintenance and querying of SW data. Such systems are informally known as triple stores, since they are specifically tailored for the storage of RDF statements, which are also referred to as triples. This sort of collaboration between database and Semantic Web specifies a data and information flow from the latter to the former, in the form of population of specialized databases, that often have some predefined structure, with SW data. A different, perhaps more interesting research line takes as starting point an existing and fully functional relational database and seeks ways to extract information and render it suitable for use from a Semantic Web perspective. In this case, motivation is shifted from the efficient storage and querying of existing ontological structures to problems, such as database integration, ontology learning, and mass generation of SW data, ontology-based data access and semantic annotation of dynamic Web pages. These problems have been investigated in the relevant literature, each one touching on a different aspect of the database to ontology mapping problem. Unfortunately, this term has been freely used to describe most of the aforementioned issues, creating slight confusion regarding the goal and the challenges faced for each one of them. Hence, in this research, we take a look at approaches that do one or more of the following:

27



create from scratch a new ontology based on information extracted from a relational database



generate RDF statements that conform to one or more predefined ontologies and reflect the contents of a relational database



answer semantic queries directly against a relational database



discover correspondences between a relational database and a given ontology

3.18 Motivation and Benefits The significance of databases from a Semantic Web perspective is evident from the multiple benefits and use cases a database to ontology mapping can be used in. After all, the problem of mapping a database into Semantic Web did not emerge as a mere exercise of transition from one representation model to another. It is important to identify the different motivations and problems implicating interactions between relational databases and SW technologies, in order to succeed a clear separation of goals and challenges.

3.19 Semantic annotation of dynamic web pages An aspect of the Semantic Web vision is the transformation of the current Web of documents to a Web of data. A straightforward way to achieve this would be to annotate HTML pages, which specify the way their content is presented and are only suitable for human consumption. HTML pages can be semantically annotated with terms from ontologies, making their content suitable for processing by software agents and web services. Such annotations have been facilitated considerably since the proposal of the RDF a recommendation that embeds in XHTML tags references to ontology terms. However, this scenario does not work quite well for dynamic web pages that retrieve their content directly from underlying databases: this is the case for content management systems (CMS), for a, wikis and other Web 2.0 sites Dynamic web pages represent the biggest part of the World Wide Web, forming the so called Deep Web, which is not accessible to search engines and software agents, since these pages are generated in response to a web service or a web form interface request. It has been argued that, due to the infeasibility of manual annotation of every single dynamic page, a possible solution would be to “annotate” directly the underlying database schema, insofar as the web page 28

owner is willing to reveal the structure of his database. This “annotation” is simply a set of correspondences between the elements of the database schema and an already existing ontology that fits the domain of the dynamic page content. 3.20 Heterogeneous database integration The resolution of heterogeneity is one of the most popular, longstanding issues in the database research field that remains, to a large degree, unsolved. Heterogeneity occurs between two or more database systems when they use different software or hardware infrastructure, follow different syntactic conventions and representation models, or when they interpret differently the same or similar data. Resolution of the above forms of heterogeneity allows multiple databases to be integrated and their contents to be uniformly queried. In typical database integration architectures, one or more conceptual models are used to describe the contents of every source database, queries are posed against a global conceptual schema and, for each source database, a wrapper is responsible to reformulate the query and retrieve the appropriate data. Ontology based integration employs ontologies in lieu of conceptual schemas and therefore, correspondences between source databases and one or more ontologies have to be defined. Such correspondences consist of mapping formulas that express the terms of a source database as a conjunctive query against the ontology (Local as view or LAV mapping), express ontology terms as a conjunctive query against the source database (Global as view or GAV mapping), or state an equivalence of two queries against both the source database and the ontology (Global Local as view or GLAV mapping). The type of mappings used in integration architecture influences both the complexity of query processing and the extensibility of the entire system. Thus, the discovery and representation of mappings between relational database schemas and ontologies constitute an integral part of a heterogeneous database integration scenario.

3.21 Ontology-based data access Much like in database integration architecture, ontology-based data access (OBDA) assumes that ontology is linked to a source database, thus acting as an intermediate layer between the user and the stored data. The objective of an OBDA 29

system is to offer high-level services to the end user of an information system who does not need to be aware of the obscure storage details of the underlying data source. The ontology provides an abstraction of the database contents, allowing users to formulate queries in terms of a high-level description of a domain of interest. In some way, an OBDA engine resembles a wrapper in an information integration scenario in that it hides the data source-specific details from the upper levels by transforming queries against a conceptual schema to queries against the local data source. This query rewriting is performed by the OBDA engine, taking into account mappings between a database and a relevant ontology describing the domain of interest. The main advantage of an OBDA architecture is the fact that semantic queries are posed directly against a database, without the need to replicate its entire contents in RDF. Apart from OBDA applications, a database to ontology mapping can be useful for semantic rewriting of SQL queries, where the output is a reformulated SQL query better capturing the intention of the user. This rewriting is performed by substitution of terms used in the original SQL query with synonyms and related terms from the ontology. Another notable related application is the ability to query relational data using as context external ontologies. This feature has been implemented in some database management systems, allowing SQL queries to contain conditions expressed in terms of ontology.

3.22 Mass generation of Semantic Web data It has been argued that one of the reasons delaying the Semantic Web realization is the lack of successful tools and applications show casing the advantages of SW technologies. The success of such tools, though, is directly correlated to the availability of a sufficiently large quantity of SW data. Since relational databases are one of the most popular storage media holding the majority of data on the World Wide Web, a solution for the generation of a critical mass of SW data would be the, preferably automatic, extraction of relational databases contents in RDF. This would create a significant pool of SW data, that would alleviate the inhibitions of software developers and tool manufacturers and, in turn, an increased production of SW applications would be anticipated. The term database to ontology mapping has been used in the literature to describe such transformations as well. 30

3.23 Ontology learning The process of manually developing from scratch an ontology is difficult, time consuming and error-prone. Several semiautomatic ontology learning methods have been proposed, extracting knowledge from free and semi-structured text documents, vocabularies and thesauri, domain experts and other sources. Relational databases are structured information sources and, in case their schema has been modeled following standard practices (i.e. based on the design of a conceptual model, such as UML or the Extended Entity Relationship Model), they constitute significant and reliable sources of domain knowledge. This is true especially for business environments, where enterprise databases are frequently maintained and contain timely data. Therefore, rich ontologies can be extracted from relational databases by gathering information from their schemas, contents, queries and stored procedures, as long as a domain expert supervises the learning process and enriches the final outcome. Ontology learning is a common motivation driving database to ontology mapping when there is not an existing ontology for a particular domain of interest, a situation that frequently arose not so many years ago. Nevertheless, as years pass by, ontology learning techniques are mainly used to create a wrapping ontology for a source relational database in an ontology-based data access or database integration context.

3.24 Definition of the intended meaning of a relational Schema Standard database design practices begin with the design of a conceptual model, which is then transformed, in a step known as logical design, to the desired relational model. However, the initial conceptual model is often not kept alongside the implemented relational database schema and subsequent changes to the latter are not propagated back to the former, while most of the times these changes are not even documented at all. Usually, this results in databases that have lost the original intention of their designer and are very hard to be extended or reengineered to another logical model (e.g. an object oriented one). Establishing correspondences between a relational database and an ontology grounds the original meaning of the former in terms of an expressive conceptual model, which is crucial not only for database maintenance but also for the integration with other data sources , and for the discovery of mappings 31

between two or more database schemas .In the latter case, the mappings between the database and the ontology are used as an intermediate step and a reference point for the construction of inter database schema mappings.

3.25 Integration of database content with other data sources Transforming relational databases into a universal description model, as DF aspires to be, enables seamless integration of their contents with information already represented in RDF. This information can originate from both structured and unstructured data sources that have exported their contents in RDF, thus overcoming possible syntactic disparities among them. The Linked Data paradigm , which encourages RDF publishers to reuse popular vocabularies (i.e. ontologies), to define links between their dataset and other published datasets and reuse identifiers that describe the same real-world entity, further facilitates global data source integration, regardless of the data source nature. Given the uptake of the Linked Data movement during the last few years, which has resulted in the publication of voluminous RDF content (in the order of billion statements) from several domains of interest, the anticipated benefits of the integration of this content with data currently residing in relational databases as well as the number of potential applications harnessing it are endless.

3.26 Classification of Approaches The term database to ontology mapping has been loosely used in the related literature, encompassing diverse approaches and solutions to different problems. In this section, we give a classification which will help us categorize and analyze in an orderly manner these approaches. Furthermore, we introduce the descriptive parameters to be used for the presentation of each approach. Classification schemes and descriptive parameters for database to ontology mapping methods have already been proposed in related work. The distinction between classification criteria and merely descriptive measures is often not clear. Measures that can act as classification criteria should have a finite number of values and ideally, should separate approaches in non-overlapping sets. Such restrictions are not necessary for

32

descriptive features, which can sometimes be qualitative by nature instead of quantifiable measures. We partition the database to ontology mapping problem space in distinct categories containing uniform approaches. The few exceptions we come across are customizable software tools that incorporate multiple workflows, with each one falling under a different category. We categorize solutions to the database to ontology mapping problem to the classes shown in Figure 3.4.

Figure 3.4: Classification of Approaches

Figure 3.5: The classification criteria and descriptive parameters used 33

3.27 Tool Structure On- To- Knowledge supports efficient and effective knowledge management by providing a tool environment powered by Semantic Web Technology. It focuses on acquiring, maintaining and accessing weakly structured information sources: 

Acquiring: Text mining and extraction techniques are applied to extract semantic information from textual information. Tool support includes ontology extraction from text (Onto Extract and Onto Wrapper).



Maintaining: RDF, XML and OIL are used for describing the syntax and semantics of semi - structured information sources. Tool support includes ontology editor (Onto Edit), and ontology storage and retrieval.



Accessing: Push - services and agent technology support users in accessing the information. Tool support includes ontology - based information navigation and querying, and ontology - based visualization of information.

34

Chapter Four 4.1 Research Methodology Most of the early software development methodologies were proposed using the function-oriented approach. Their main objective was the systematic software development that could provide user requirements at a reasonable development and maintenance cost. With the passage of time software reusability, interoperability and integration problems raised, and they became the main motivation of several object-oriented methodologies, such as the proposed and reported methodologies in the literature. But these methodologies are unsuitable to use for the development of software for the third generation web because there is a need of machine understandable semantics of web contents in this type of software systems.

4.2 Methodologies for Development of Semantic Web Based Systems Web ontology is considered as the backbone of a semantic web system as it models a domain. During modeling domain ontology, its terms are defined for making them machine understandable, and relationships between them are also defined. Semantic annotations via ontologies have already been started for the semantic web systems. It is a process that transforms a web system into a Semantic Web system by augmented their contents with metadata that formally defines and makes them machine understandable.

Semantic

Web

software

consists

of

machine

understandable content as well as human understandable content so-called web pages. In model chosen which was designed by Amjad Farooq and M. Junaid Arshad in their paper entitled Scientific A Process Model for Developing Semantic Web Systems . The sketch of model is shown in Figure 4.1. In the proposed model, the two major activities: i) Generation of web pages. ii) Construction of logical content (or ontology). Carried out in parallel. Then, their integration and testing is performed to produce a machine understandable as well as human understandable final product. The phases involved in the model are described in Figure 4.2. 35

Figure 4.1. Activities of analysis phase

Figure 4.2. The architecture of proposed model

4.2.1 Analysis Phase The analysis phase defines the requirements of the system, independent of how these requirements will be accomplished. In this phase it is determined, what are the client’s needs along with what the client wants. The deliverable result at the end of this phase is a requirement document. Since there are two types of requirements: human as well as machine understandable contents, business analyst, web engineer and ontology engineer are involved to determine and analyze these requirements. Different activities involved in this phase are presented in Figure 4.1 and each parameter is briefly described below: 36

Requirement Determination: In this phase, the target humans are identified and they are grouped into classes, having the same functional requirements. For each class usability requirements are been applied. That show in Figure 4.3.

Figure 4.3 Creating the site map Knowledge Acquisition: This activity is the prerequisite of ontology construction track as mentioned in Figure 4.2. Several relevant knowledge sources such as organization hierarchy, university councils, distinguished research, university facilities, internal documents, etc. are collected and analyzed. Figure 5.5 illustrates that.

37

Requirements Modeling: The output of requirements determination activity is rewritten using controlled vocabulary in some formal way according to the modeling standards. Figure 4.4. illustrates that.

Figure 4.4 Wireframe of University of Gezira Formal Specification: The descriptive knowledge obtained in the knowledge acquisition activity is organized in classes, subclasses, properties, sub-properties, relationships, constraints & rules using controlled vocabulary. Figure 4.5 shows ontology for the classes (Faculties, Departments, Staff, Students, Persons, Programs, Subjects and Grade points) in University of Gezira website ,figure 4.6 shows ontology for all students operation, and Figure 4.7: Taxonomy of Persons within in University of Gezira website. 38

Figure 4.5: ontology for classes (Faculties, Departments, Staff, Students, Persons, Programs, Subjects and Grade points)

Figure 4.6: ontology for all students operation in University of Gezira website

39

Figure 4.7: Taxonomy of Persons within the University of Gezira website

4.2.2 Design Phase As stated in the previous phase, there are two types of requirements for a semantic web application: one for human and other for machine. For the first type of requirements the following components are designed by using pre-defined software engineering standards for the output of previous phase i.e. Requirements modeling document: Navigation, database, pages, templates and presentations. Whereas, for the second types of requirements. i.e., machine understandable content generation. Classes are categorized in term of domains. An ontology diagram is produced for each domain. Rules are described in terms of constraints and triggers , when occurs and what actions need to performed. Since ontologies can be reused and shared, so before designing a new ontology for any domain, first of all existing ontologies are examined in order to find the suitable ontology for that domain. If some relevant ontologies are found, they are included in the output document of this phase. The relevant terms are determined and their mappings to the compatible term in domain model are performed. RDF graph annotated with integrity constraints, domain, range specifications, and cardinalities is produced. Several tools are available for the assistance of ontology creation, such as Protégé that was applied in this thesis. This is very simple tool, it allows you to graphically create ontology document in OWL and RDFS, and so you can create valid documents quickly and easily. Similarly, for 40

developing pages, applets, procedures for business logic, number of tools are available those allow developers to make development very quickly and easily. Figure 4.8 below shows the OWL visualization of a section of the

person

ontology within the University of Gezira website which covers the sub-class “Staff”. An instance of any subclass could be created such Academic Staff and Technical Staff.

Figure 4.8 : OWL visualization of sub-classes "Staff" in the person ontology within the University of Gezira website An RDF dataset (that is, a RDF graph) can be viewed as a set of the edges of such a graph, commonly represented by triples (or statements) of the form:

Figure 4.9: A simple RDF graph 41

Chapter Five Design and Implementation 5.1 Creating Website Information Architecture and Content This chapter is aimed to upgrade the design of the University of Gezira website to cope up with the international universities web standards through organizing the information in conceptual spaces according to its meaning by using Semantic Web and ontology. In the first step we focus on the steps for creating effective site organization and navigation, also known as information architecture (IA) which refers to the structure and organization of the website. IA describes the ways in which the different pages of the site relate to one another and ensures that the information is organized in a consistent and predictable way on each page. Through the process of developing information architecture (IA), the site map of the contents of the University of Gezira will be developed. The wireframe that sketch the University of Gezira is depicted in figure 4.4 and its site map is shown in figure 5.1.

Figure 5.1: Site map of University of Gezira website 42

The wireframe that sketch the old University of Gezira Website is depicted in figure 5.2 and its site map is shown in figure 5.3.

Figure 5.2: Wireframe of University of Gezira old website

Figure 5.3: Site map of University of Gezira old website

43

Information architecture in this way helps the visitor's to find key information quickly. It also will make the website more coherent and satisfying. Additionally, intuitively organized information architecture to ensure that all of the phases of the website development run smoothly and efficiently. In fact, it can prevent timeconsuming and costly alterations to the visual design and site development by identifying required features, the number and location of navigational links and the placement of content early in the process. If the site is being built in a content management system that allows editors to add their own pages, the site will grow quickly.

5.2 Create Information Architecture 5.2.1 Step1. Defining key stakeholders We need to clarify the key stakeholder's and determining the purpose of the website. The key stakeholders are the people who visit university web portals looking for information regarding students, international student applicants, graduate students, alumni, faculty, academic support staff, all campus users, news media, and external visitors. The university website is reflects its global performance, the quality of its departments and services, the impact of its outputs and its international prestige.

5.2.2 Step 2.Identifying user's goals and expectations Students looking for course information, change in lecture times, laboratory tables, e-mail access communication with teacher...etc. Alumni looking for job, services and communication with their colleagues...etc. Staff members are looking for e-mail, academic regulations, theses and dissertations ...etc. The goal of effective Web design is to anticipate the visitor's needs. To accomplish this we arranged and labeled information a way that the target audience is expected to see it.

5.2.3 Step3. Defining site’s content areas Defining content areas help us to development navigational structure for website. In University of Gezira the content that reflect university is its global performance, the quality of its, includes information about university, vice chancellor message, vision ,message ,objectives, history of the university ,organization hierarchy ,university guide ,university facilities ,university endowments, university awards, university agreements, 44

old website, contact information with university ,director office , deputy director ,vice office, university councils, university deans ,deanship ,central departments ,special departments ,university participations, faculties ,departments, institutes, centers, students and their activities, alumni & related activities, staff and their services ,research, dissertations & theses , journals, university media, and electronic services, university news, the impact of its outputs in community and its international prestige.

5.2.4 Step4. Organizing the content areas In this step, we will organize the content areas compiled in the step 3, into groups of similar or related topics. These groups will be given temporary names that later will be refined to become the navigation menu items. This activity will help us group and label content areas so that your navigation will be more intuitive for visitors.

5.2.5 Step5. Creating the site map Now, we create and validate the site map (a visual representation of the content areas). Figure 5.4 illustrates how to organize the website in a hierarchical way. In this type of structure pages have a parent/child relationship. Not every page has a child, but all pages have a parent.

Figure 5.4: Illustrate Children and Parent page in site map of University of Gezira 45

5.2.6 Step6. Outlining the navigational structure In this step we take the site map of University of Gezira that we have created in Step 5 and draw it to emulate the navigation scheme in figure 5.5. The subpages are listed under the main content area headings. The navigational items of site should not point to other sites, nor should they point to Acrobat (.pdf) files, Microsoft Office documents or other non-HTML files. Doing this can be disorienting for the visitor of website and can be problematic for those with slow connections, and this can reduce the visitors which affects the ranking of website.

About UofG

Admin

Research

Faculties

vision-messageobjectives

officedirector deputydirector

history

vice-office

organizationhierarchy

universitycouncils universitydeans

rector-message

university-guide universityfacilities universityendowments universityawards universityagreements

journals

Institutes participations dissertations & theses distinguished research

University Campuses Impact E-mail

deanship centraldepartments specialdepartments

old-website contact-us

Figure 5.5: Sketch of a navigation scheme created from a site map of University of Gezira

46

This is quick menu in University of Gezira website.

This is main menu for University of Gezira website created from a site map.

Figure 5.6: Main menu and quick menu in University of Gezira Links to other websites and documents should be placed in central content area. And related links should be placed in footer that helps visitors to browse the site easily. The table of contents should not be cluttered with items that do not describe the main content areas of the site. Figure 5.7 show example of related links section.

47

The central content area can contain links to other sites and documents.

This is a link to another section within this site.

This is a “related links” area.

This is a link to a page outside of this site

Figure 5.7: Example of a related links section

5.2.7 Step7. Labeling the content areas It is important to give accurate and meaningful labels to content areas. Visitors are clicking on words, so the words need to clear make sense. In this research I tested

48

nomenclature by showing the navigation scheme outlined in Step 6 for the people how so interested. And they explained it's perfectly clear.

5.2.8 Step8. Creating wireframes A wireframe is a sketch or blueprint that closely represents how the areas of a page will be organized. It is important use the wireframes as guides. This will help to ensure that information architecture is not inadvertently revised or obscured later during the design and development processes. Figure 4.4: displays the wireframe of University of Gezira

5.3 Information architecture standards The purpose of information architecture is to create an organization for site content that will be as intuitive and easy for visitors to use as possible. To achieve this goal we improved the structure of the site to make visitors find what they seek and predict what will happen when they click on an item.

5.3.1 Naming conventions Having a logical naming convention helps visitors to know where they are and how to return. So, we use related and easily words for the Web address, menu labels and page headers. Figure 5.7: shows good consistency between the Web address, page header and menu label. This naming convention is especially important for subpages. Not all visitors start from the home page, so subpages need to provide some context too.

5.3.2 Website title One of the important things to make visitors know where they are, we set title prominently displayed in the top of every page in website. In the case of University departments and offices, the site title most often is the name of your office or department. The site title usually is located in the same place on every page.

49

Page title

Site title

Web address

Menu label Figure 5.8: Example of consistent naming conventions

5.4 Design Guidelines 5.4.1 Clearly identify university on every page The name of university should be clearly visible on every page. Because not everyone arrives at website from the homepage, many visitors will arrive on internal pages via search. In sub sites and microsites especially, it is essential that visitors know which university they’re looking at. While it might be obvious to you that the faculty of mathematical and computer sciences is a part of this university, not everyone will know that. By having your full university name shown prominently on each site, you make it easy for visitors to identify the university website, and look at things, which it is looking for .As its figure 5.9. 50

5.4.2 Use images that reflect your university’s values and priorities Some visitors make judgments about the university based on the images that displayed in the website.(see figure 5.9)

Figure 5.9: Use images in main page for University of Gezira website 5.4.3

Make about university page The About Us page is one of the top places where prospective students go when

deciding if a university is a good fit for them. This area is a missed opportunity on many 51

university sites, with too much content that is dull, uninformative, and feels like generic. Improve this page by leading with an informative summary of university, and write this summary in plain language offer an easy-to-scan fact list. Figure 5.10 illustrates about University of Gezira.

Figure 5.10: About University of Gezira

5.4.4 Highlight strengths and achievements When first looking at new university, visitors want to know why this university is distinct. Gather those statistics, rankings and awards, and make them easy to find it (for example, on your About Us page), that collects a large number of visitors. We know that users scan pages, they rarely read full text. So, don’t bury valuable, potentially persuasive, data in a long, dense paragraph. This goes for the global website

52

as well as individual college or department websites. Figure 5.11: illustrates strengths and achievements in University of Gezira.

Figure 5.11: Strengths and achievements in University of Gezira

5.4.5 Make it easy for visitors to view a list of majors and academic programs Many of users didn't realize that the university offered the program that they were looking for even when it did. A reason was that people don’t know which degrees belong to which school when they did not find a program where they expected it to be, they assumed it simply isn't offered. Instead of forcing users to guess where their program of interest, we offer the option to view all the majors and programs. Figure 5.11: above in footer page illustrates all of these services

53

5.4.6 Provide information about job placement after graduation, and link to it from the alumni section of the website When evaluating college, another top concern for both prospective students and parents is whether the investment in education will pay off after they graduate. In our research, the first place where users went to find this information was the Alumni page, which they associate with all things after college. Universities should provide data about what graduates are doing after college, with numbers and sources to support those claims.


 Figure 5.12: Illustrates information about alumni and job placement in University of Gezira. 54

5.4.7 Prepared visitors to search for information about university on external websites When visitors to the website can’t find what they’re looking for, they quickly look to external sites for help. The visitors, whose research skills are not yet fully developed, are especially quick to turn to an external search to find what they’re looking for, and we should also make sure that the information on those external sites is accurate and up to date and developed in footer page. Figure 5.13: illustrates external sites in University of Gezira website.

Figure 5.13: External sites in University of Gezira website 55

5.5 Improve the presentation of data We dealt previously with several standards to improve the university website, but still we have a problem. The sheer amount of data on the web, together with its distributed, redundant and inaccurate nature, makes using the information within rather cumbersome. So we will deal with this problem by applying new technology called Semantic Web. The Semantic Web offers visitors the ability to work on shared meaningful knowledge representations on the web. And we will use ontology of which facilitates the management, student's faculty members and other stake holders in the university.

5.6 Tools for Ontology Development Ontology is the main term in the Semantic Web. Protégé tool [42] is the most popular and widely used tool for ontology development. Here we use this tool and Microsoft Visio to developing Ontology for University of Gezira.

5.7 Ontology Development for University of Gezira Figure 5.14 (A), Figure 5.14 (B), Figure 5.14 (C) shows the whole layout of the University of Gezira ontology proposed in this research.

Figure 5.14 (A): General Layout of University of Gezira Ontology 56

Figure 5.14 (B): Left Layout of University of Gezira Ontology

57

Figure 5.14 (C): Right Layout of University of Gezira Ontology

58

Figure 5.15: Implementation of University of Gezira Ontology in Protégé_4.0.2 The University of Gezira has many faculties and each of these faculties has more than one department, each department offer programs and each program has many subjects. The University keeps track of information about persons either students or staff .And all students study at least one subject. These subjects taught by staff and the students are evaluated using grades for these subjects. A grade report must be generated for each student that lists in the grade point table. Figure 4.5 shows ontology for the classes (Faculties, Departments, Staff, Students, Persons, Programs, Subjects and Grade points) in University of Gezira website.

59

5.8 Vocabularies A vocabulary defines the semantics of entity types and their responsibilities, the taxonomical relationships between entity types, and the ontological relationships between entity types. Semantics is used for meaning - when we're defining the semantics of something we're defining its meaning. Taxonomies are classifications of entity types into hierarchies; an example Figure 4.7 illustrates taxonomy for person within the University of Gezira website. The figure reveal several interesting aspects. 

It takes a “single section” approach to classes, because we're exploring relationships between entity types but not their responsibilities.



It uses UML to generalization set concept, basically just an inheritance arrowhead with a label representing the name of the set.



There are three generalization sets for Person: Nationality, Role, and Gender. These generalization sets overlap - a person can be classified via each of these roles (e.g. someone can be a male foreign student). This is called multiple classifications.

Figure 5.16 shows how the various classes in Person ontology within the University of Gezira relate to each other within the Protégé_4.0.2 application. Unlike the class diagram, the protégé class hierarchy shows the various sub-classes and relations that were added to the parent class in the Person ontology. For instance: Staff is a sub-class to Person as relation of Person (Staff). Figure 5.17.

Figure 5.16: Protégé_4.0.2 Class hierarchy of person ontology within the University of Gezira website 60

Figure 5.17: Uses of person ontology within the University of Gezira website Given the state of the RDF and the OWL standards, the building of a rule language is not just a cheap add-on to the standards created so far. Ontology languages are good for describing knowledge adhering to the Open World Assumption 5.9 RDF and RDF Schema The Resource Description Framework (RDF) defines the data model for the Semantic Web. Driven by the goal of least possible commitment to a particular data schema, the simplest possible structure for representing information was chosen – labeled, directed graphs.

61

62

Figure 5.18: RDF/XML rendering: for persons within the University of Gezira website 63

Figure 5.19: OWL Functional syntax rendering: for persons within the University of Gezira website

64

Chapter Six 6.1 Results and Discussions This research gives a detailed methodology to design the University of Gezira website according to the Semantic Web or web 3.0 standards to enhance the quality of the information that will be presented in the Web site. Microsoft Visual Studio 2010 is used to design the web pages, and SQL server 2008 is used to host the database. In the new designed web site the information in the university web site will be given welldefined meaning, and this will enable computers and people to work in co-operation since the vision of the Semantic Web as a natural extension of the World-Wide Web is to enable the machine to understand the human inputs. In this new Semantic Web , XML is no longer just the universal format for structured documents and data on the Web, but Ontology information that cannot be represented by XML Schema is carried by a new ontology language SWOL. This web site is fully functional for as many visitors as possible across a wide range of browsers, devices and operating systems, without negatively affecting the performance of website. And the content is viewable, and page design degrades gracefully, depending on how well the visitor's browser adheres to common web standards.  All documents, including any web page in this site published in formal grammars.  JPEG, GIF or PNG files for images.  XHTML 1.x - Strict or Transitional is used and web coding is meet W3C  XHTML validated standards as close as possible.  CSS2.1 – CSS code meet W3C CSS validated standards as close as possible. To meet these standards the code validate with no errors, although some “warnings” could be acceptable.

 UTF-8 character encoding - this is to help ensure consistency of data across government and to best enable multilingual support.  We used style sheets to control the layout and presentation of page and elements. Instead of tables and frames.  The background colors are contrast with text color.  Avoided patterned backgrounds that make text difficult to read. 65

 Domain names and web aliases create a readable name to help visitors remember how to get to a website. They also make it easier to type the URL into a browser.  Names which are more than one word is meaningful and, where possible, the words separated by a dash (-). The use of underscores (_) is discouraged as they are difficult to detect in a web address bar.  The following structure applies depending on the relationship of the University of Gezira website to a faculty or service division:  www..uofg.edu.]sd – distinct sites with their own identity Example: www.fmcs.uofg.edu.sd  www.. uofg.edu.sd / – subsections of an existing site (friendly or short URLs for reference purposes) Example :www.fmcs.uofg.edu.sd/cs or  www...uofg.edu.sd– distinct site within a faculty or service division (with their own identity). Also include web applications associated with that faculty or service division. Example: www.scholarships.fmcs.uofg.edu.sd  The following guidelines were applied to any content designed with intent of uploading to the University of Gezira website. Imagery: Images need to be optimized (using a graphics program such as Adobe Photoshop) for use on the web. There are three standard file types used on the Internet: 

JPEG - used for standard photograph format.



GIF - images with a smaller number of colors. Not be used for larger images or standard photograph format.



PNG 8bit (256 colors) and 24 bit (16m colors) – images of any size with a smaller number of colors. 24bit is best for images with gradient or transparency. Not to be used for standard photograph format.

Example: images in slider show and images in pages just in JPEG, GIF and PNG format.

66

 Alternative text: All non-text content that is presented to the user should have a text alternative that serves the equivalent purpose for example - organization-hierarchy, except for the situations listed below: 

Controls Input: If non-text content is a control or accepts user input, then it has a name that describes its purpose.



Time-Based Media: If non-text content is time-based media, then text alternatives at least provide descriptive identification of the non-text content for example video that describes University of Gezira.



Decoration, Formatting, Invisible: If non-text content is pure decoration, is used only for visual formatting, or is not presented to visitors, then it is implemented in a way that it can be ignored by assistive technology for example images that used in header and footer.

 File naming rules below have been applied: 

All the file names now are in lowercase letters.



The files names now are meaningful; consist of alphanumeric characters, with the correct file extensions.



Where possible the file name now is kept simple and is one word only. Where more than one word is required, the file name now is not have spaces but separated by a dash (-). The use of underscores (_) and camel case is discouraged as they are difficult to detect once the name becomes a URL.



The name now is not exceeding sixty characters.

Example: about-uofg.pdf  Folder naming rules below have been applied: 

All folder names now are in lowercase letters.



Separate folders now are maintained for each element of the website.



Where possible the folder name now is kept simple, consists of alphanumeric characters, and is one word only. Where more than one word is required, the file name now is not have spaces but separated by a dash (-). The use of

67

underscores (_) and camel case is discouraged as they are difficult to detect once the name become a URL. 

Every folder now is consisting of a default document.



The name now is not exceeding twenty-five characters.

Example: about-uofg When publishing the new designed web site, the webometrics measure for the university is expected to be enhanced according to what is depicted in the following table. Table: 6.1 Expected Criteria for Evaluating Web Resources

Criteria

University of Gezira current website High Medium Low

The URL suggests a reputable affiliation with regard to the topic--personal or official site; type of Internet domain. The information is clearly presented. Currency: the information presented is accurate Usability: the site is welldesigned and stable. The site organization is logical and easy to maneuver. The content written at a level that is readable by the intended audience. Compatibility: Browsers, screen resolution. Using file naming rules (file names should be in lowercase letters ,files names should be meaningful, consist of alphanumeric characters, with the correct file extensions, the name should not exceed sixty characters) Using folder naming rules (folder names should be in lowercase letters, folder names should be meaningful, separate folders should be maintained for each element of the website)

University of Gezira website proposed high medium low

√ √ √

√ √



















√ √



68



6.2 Conclusion In this research, we dealt with the Semantic Web briefly, been subjected to this subject in terms of the previous studies and the concept of the Semantic Web, as we talked of the importance of the Semantic Web, and techniques used by, and relationship of Semantic Web with ontology and information retrieval. Through the topics that we've had, it turns out that the Semantic Web is already a revolution of new information which is indispensable, but it must work to develop programs and techniques needed him, so that can make the most of it, both in universities, or in any other fields . It is also necessary to take care of those who are working on the development of the Semantic Web and utilization of the human element, which is one of the most important elements of the development of the Semantic Web, in addition to providing the funds necessary for the development process, it also was upgraded the University of Gezira Websites to Web3.0 standards and according to the universities’ website standards by organizing the information in conceptual spaces according to its meaning by using Semantic Web and ontology.

6.3 Recommendation Design website for scientific research using Semantic Web technology to facilitate access to information and enhance the University of Gezira website.

69

References [1]. THURAISINGHAM, B. 2004. Security standards for the semantic web Suite 1125. [2]. E. PANTANO, R. S., AND M. VIASSONE 2011. An exploratory study of the tourist-computer interaction: the role of web site usability on hotel quality perception. International Journal of Digital Content Technology and its Applications, vol. 5, pp. 208-216. [3]. CORVELLO, E. P. A. V. 2010. Digital contents management for improving consumers’ experience. International Journal of Digital Content Technology and its Applications. [4]. A. CARO, C. C., AND M. A. MORAGA 2011. Are Web Visibility and Data Quality Related Concepts. Internet Computing, IEEE, vol. 15, pp. 43-49, 2011. [5]. WIKIBOOKS.ORG/WIKI 2011. Semantic_Web. Vision 15-7-2014. [6]. WANG, Y. 2003.Information Retrieval and Semantic Structure Matching for Assessing Web-Service Similarity. [7]. STRASSER, A. 2004. A Semantic Web Approach to Implementing an OAI Data Provider Using BIBTEX Bibliographic Data in September. [8]. KALEM, G. JUNE 2005. SEMANTIC WEB APPLICATION: ONTOLOGYDRIVEN RECIPE QUERYING [9]. FAROOQ, A., ARSHAD, M. J., & SHAH 2010. A layered approach for similarity measurement between ontologies. Journal of American Science, 6(12), 69-77 [10]. KARGAR M. J. Role of Presentation Quality in University Website Ranking A Case Study in IRAN. [11]. RDFA, M. N. A. O. T. D. B. 2011. as Semantic Markup and Web Visibility [12]. AL-OBAIDY, H. S., & AL HEELA 2012. Annotation: An Approach for Building Semantic Web Library. Appl. Math, 6(1), 133-143.. [13]. MEYMANDPOUR, R., & DAVIS, J. G. 2013. Ranking Universities Using Linked Open Data. In LDOW. [14]. HTTP://WWW.UOFG.EDU.SD 12 May 2014. [15]. HTTP://WWW.LIBRARY.KENT.EDU/PAGE/10475 23 April 2014. page/10475. [16]. RAUHVARGERS, A. 2011. global university rankings and their impact eua report on rankings 70

[17]. (IJCSIT), R. K. P. 2014. International Journal of Computer Science and Information Technologies. Vol. 5 (1) , 2014, 580-584 www.ijcsit.com 580. [18]. 14 May 2014.

http://www.webometrics.info.

[19]. 14 May 2014.

http://www.4icu.org.

[20]. 2014.

Wikipedia Times Higher Education World University Rankings.

[21]. NIYITEGEKA, M. Towards a website Evaluation Framework for Universities" Case Study Makerere University. pp 323. [22]. 23 April 2014. http://www.nngroup.com/articles/university-sites. [23]. TIM BERNERS-LEE, J. H., AND ORA LASSILA may 2001. The semantic web. Scientific American [24]. AGRAWAL, R., AND RAMAKRISHNAN SRIKANT Fast algorithms for mining association rules. Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994. [25]. DAS, S. 2007. Role of semantic web in the changing context of digital environment. [26]. ZHANG, J. 2007. Ontology and the Semantic Web. [27]. DACONTA, M., OBRST, L., SMITH, K 2003. The Semantic Web. [28]. S.STROKA.: 2005. Knowledge Representation Technologies in the Semantic Web. [29]. 14 May 2014.

Semantic

Web

Layer

http://en.wikipedia.org/wiki/Semantic_Web. [30].

Semantic Web Architecture http://obitko.com/tutorials/ontologies-semanticweb/semantic-web-architecture.html.

[31]. 23 April 2014. http://www.nngroup.com/articles/university-sites. [32]. S. DECKER, F. V. H., J. BROEKSTRA, M. ERDMANN , D. FENSEL, I. HORROCKS, M. KLEIN ,S . MELNIK The Semantic Web - on the respective Roles of XML and RDF. [33]. MIHOUBI, A. S., AND M. SIMONET An Ontology Driven pproach to Ontology Translation. In Proceedings of DEXA, 2000, pp.573-582. [34]. FENSEL, D. 2001. Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce.

71

[35]. TAYE, M. M. 2010. Understanding Semantic Web and Ontologies: Theory and Applications. arXiv preprint arXiv:1006.4567 [36]. M. PAOLUCCI, T. K., T.R. PAYNE, AND K.P. SYCARA 2002. Semantic Matching of Web Services Capabilities. In Proceedings of International Semantic Web Conference, pp.333-347. [37]. GANGEMI, A., NAVIGLI, R., VELARDI, P (ODBASE 2003), Catania, (Italy), (2003). The OntoWordNet Project: extension and axiomatisation of conceptual relations in WordNet. International Conference on Ontologies, Databases and Applications of SEmantics. [38]. GANGEMI, A., CATENACCI, C., BATTAGLIA, M 2004. Inflammation Ontology Design Pattern: an Exercise in Building a Core Biomedical Ontology with Descriptions and Situations.D.M. Pisanelli (ed.) Ontologies in Medicine, IOS Press, Amsterdam. [39]. GRUNINGER, M., AND FOX, M.S 1994. The Role of Competency Questions in Enterprise Engineering. Proceedings of the IFIP WG5.7 Workshop on Benchmarking - Theory and Practice, Trondheim, Norway [40]. MASOLO, C., A. GANGEMI, N. GUARINO, A. OLTRAMARI AND L. SCHNEIDER 2004. WonderWeb Deliverable D18: The WonderWeb Library of Foundational Ontologies. [41]. RECTOR, A. L., ROGERS, J.: PATTERNS 2004. Properties and Minimizing Commitment: Reconstruction of the GALEN Upper Ontology in OWL. [42]. MALVIYA, N., NISHCHOL MISHRA, AND SANTOSH SAHU Developing University Ontology using Protégé OWL Tool: Process and Reasoning. International Journal of Scientific & Engineering Research.

72

Suggest Documents