2013 IEEE Seventh International Symposium on Service-Oriented System Engineering
Semantic Annotation for Web Services Based on DBpedia Zhen Zhang1,2, Shizhan Chen1,2,*, and Zhiyong Feng1,2 1 2
Tianjin Key Laboratory of Cognitive Computing and Application School of Computer Science and Technology, Tianjin University Tianjin, 300072, China {zhenzhang, shizhan, zyfeng}@tju.edu.cn
interlinking a data set with existing data sets according to the Linked Data principles, the data have been increased rapidly in recent years. As a crystallization point for the Web of Data, the DBpedia knowledge base [3] becomes one of the central interlinking-hubs of the emerging Web of Data. DBpedia extracts structured information from Wikipedia and automatically evolves as Wikipedia changes. So the dataset covers a wide range of encyclopedic topics, describing millions of things, most of which are classified in a consistent Ontology, the DBpedia Ontology1. Its classes form a cross-domain and shallow subsumption hierarchy. The DBpedia Spotlight2 is a DBpedia application for annotating text documents with DBpedia URIs, allowing users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures [4]. Therefore, the abundant DBpedia knowledge base including the large amount of interconnected data and its cross-domain ontology, along with its applications, open new perspectives in the solution of the Web service annotation.
Abstract—The vast majority of Web services on the Internet lack explicit and sufficient semantic information. As a result, we cannot provide all the relevant services during service discovery, and have difficulty in service composition. In this paper, we propose an automated approach to semantic annotation for Web services based on the DBpedia knowledge base. Through rich, open Linked Data resources, and taking advantage of the DBpedia Ontology, a consistent and cross-domain ontology, and its application DBpedia Spotlight, we can match the appropriate Linked Data concepts to the corresponding parameter concepts of Web services. The annotated Web services contain the same semantic relationships as those within their corresponding Linked Data resources. Moreover, we also present an evaluation framework in accordance with our annotation approach. We performed experiments on two different datasets which incorporate altogether 30743 parameter concepts. An analysis of our experimental results indicates that our methods produce a high annotation rate with great accuracy. Keywords—Semantic Annotation;Web Service; DBpedia; Linked Data; Semantic Web
I.
In this paper, based on the DBpedia knowledge base, we propose an automated approach to semantic annotation for Web services. The purpose of our work, on one hand, is to present a novel method of semantic annotation for the inputs and outputs of Web services. By taking advantage of the cross-domain ontology, Web services will be annotated without classification [5] and time-consuming construction of the service-oriented ontologies [6]. On the other hand, we present an evaluation framework in accordance with our annotation approach and apply it to semantic annotation for a number of publicly available Web services. In order to provide an unbiased evaluation of the annotation method, we performed experiments on two different datasets. The evaluation results suggest that our method is effective producing high annotation rate with great accuracy.
INTRODUCTION
Web services, regarded as the best implementation of Service-Oriented Architecture and a general solution for the development of complex distributed applications, are offering various functionalities in many areas such as e-commerce and communications. However, due to the fact that Web services on the Internet described by the standards such as WSDL are usually syntactical lacking sufficient semantic information, it is difficult to find services that satisfy desired functionalities and combine relevant services into more complex and appropriate ones. The service discovery results may not accurately match the given service request by keyword-matching technologies and the syntax-based Web service are not good enough for automated Web services composition. Therefore, semantic annotation is required in order to provide the missing ingredients for Web services.
The rest of the paper is organized as follows: Section II reviews the related work in the area of Web service semantic annotation. Section III describes our annotation approach in detail. The evaluation measures of the proposed approach and experimental results are presented in Section IV. Finally, conclusion and future work are presented in Section V.
At the same time, deriving from research on the Semantic Web [1], the Linked Data technologies [2] are a set of best practices for publishing and connecting structured data on the Web, leading to the creation of an unbound, global data space containing billions of RDF triples – the Web of Data. Since anyone can participate simply by publishing and
II.
Various efforts have been made to enriching Web services with semantic annotations. The work described in [8] 1 2
978-0-7695-4944-6/12 $26.00 © 2012 IEEE DOI 10.1109/SOSE.2013.34
RELATED WORK
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Available at: http://wiki.dbpedia.org/Ontology Available at: https://github.com/dbpedia-spotlight/dbpedia-spotlight
model, also the source of the semantic information is the ontology. For our concern, the description of the model for Web service semantic annotation based on DBpedia is as follows:
proposes the Semantic Web Service (SWS) which is intended to provide a better support for service discovery, composition, and execution. By combing Web services and semantic Web technologies, SWS is characterized by a number of conceptual models such as OWL-S, Web Service Modeling Language (WSML) [9], Web Service Modeling Ontology (MSMO), Web Services Description Language Semantic (WSDL-S) [10] and Semantic Annotations for Web Services Description Language (SAWSDL) [11]. These semantic descriptions of Web services which have already been proposed to W3C are essentially incompatible due to differences in representation languages and concepts, as a result, the adoption of SWS is influenced and its impact is limited that only accessible to highly trained experts [12]. Specially, The METEOR-S semantic annotation framework [5] proposes a semi-automatically semantic annotation method for Web service descriptions with ontologies, and also uses domain ontologies to categorize Web services into domains. Besides, the ASSAM toolset [13] classifies Web services by applying machine learning techniques, and then derives a mapping between a collection of web service descriptions and ontology, using string similarity metrics. Early work [7] proposes a classification and annotation method for constructing the Service Network, which is a semantic relation based Web service infrastructure, with the help of domain experts and WordNet. The above techniques require taxonomies for classification, and they are not automatic enough bringing little benefits for the process of automated service discovery. They need special ontologies for different Web services which cost many extra works otherwise the annotation results cannot be rational. In particular, they are so limited for our open world on the Web. Considering the trends and development of Semantic Web, especially the Web of Data, [12] proposes the concept Linked Services which outlines two ideas: publishing service annotation in the Web of Data, and creating services for the Web of Data. However, it devotes efforts to presenting principles about publishing services with Linked Data, and focusing on building models such as Minimal Service Model for publishing new services encouraged by the Linked Data principles, rather than providing a specific annotation method. Thus, an automated approach to semantic annotation for Web services based on Linked Data, DBpedia as a case, is on its way. III.
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Service level: The original Web service. It incorporates the general information of the service, such as the service name, protocol, URI, service description, execution time and functionality. A service can consist of at least one or several interfaces that implement the functions, which is represented by solid lines in Fig. 1.
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Interface level: An interface is the basic functional unit of a Web service which is of much concern during the process of the service discovery and composition. We call it, in another way, the operation. An operation is the atomic process of one service; this also indicates that the interface level contains all the functions that the service can supply.
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Parameter level: Parameters come from the inputs and outputs of the services. An input is what the Web service requires to produce the expected answer; meanwhile, an output is what the Web service provides to response the request. The parameter concepts of simple type are always the leaf node elements of the Web services. This level is where the semantics should be attached.
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DBpedia instance: Millions of entities that extract from Wikipedia. They are assigned a URI according to the pattern http://dbpedia.org/resource/name. DBpedia URIs interlink to each other by RDF triples, and most of them are mapped to the DBpedia Ontology [14]. These instance data can bring semantics to the parameters.
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DBpedia class: The latest version of DBpedia Ontology consists of 359 classes defined in the namespace http://dbpedia.org/ontology/. Additionally, in this level, we can carry out queries and inferences about the model based on the DBpedia Ontology to mine the relations between the services, which are significant steps for constructing the semantic network for Web services.
APPROACH
In this section, we introduce our approach to semantic annotation for Web services using the DBpedia resources. The aim of our work is how to enhance the Web services with semantics in Linked Data, more specifically, is how to attach the appropriate DBpedia URIs to the inputs and outputs of the Web services. A. Service Annotation model Considering finding a model for service annotation, technically, each Web service can be divided into three levels. And three levels correspondingly on the semantic level, as shown in Fig. 1. Meanwhile, at the bottom of the
Figure 1. Model for Web service semantic annotation.
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C. Details of Annotaion The semantic annotation of the leaf node is described in Algorithm 1. First of all, we refer to our annotation vocabulary in case that we are doing a bit of extra work. If we can find the information of this parameter in our repository, we obtain the stored ontology concept that is attached to the parameter, if not, after annotating it according to the following method, we store this parameter and its associated annotation information to the repository in the end, as 2-4 and 9 lines indicate. The following step is cleaning the parameter. Because of the diverse definition of different service developers, the parameter names are irregular and text processing is in demand. The aim of this work is to find the key words in an irregular string of words that are able to explain the parameter most clearly. By analyzing thousands of parameter names in WSDL, we propose a method for processing the parameter names described as follows:
B. Overview of the Proposed Approach The key steps of the proposed approach for semantic annotation based on DBpedia are illustrated in Fig. 2. The first step of the proposed approach involves parsing the Web services. This step includes two tasks: (1) validation of the legal WSDLs that are meaningful to users, and (2) acquisition of elements in three levels of each Web service as described in the model. This is a fundamental step for semantic annotation. The next two steps deal with the parameters including parameter refinement and cleaning. From the model mentioned above, we can see that the parameter level is attached with semantics by mapping the corresponding DBpedia instance data. We make this choice for two reasons, one of which is that the leaf elements represent maximally fine-grained data objects. Once these parameters have been annotated, the resulting annotations can be propagated to other two levels as required. Second, from the service requestor’s perspective, the inputs and outputs are the most relevant to them, because the parameter level contains what the requestor need to provide and what the service can provide for them. This choice does not imply that we only analyze the parameters of Web services. In fact, in order to extract contextual information that is used to derive semantic annotations for parameters, we also exploit the hierarchical structure of WSDL interfaces and XML schemas. Therefore, the parameter concepts of complex type should be decomposed into their child parameters in the process of parameter refinement. Since parameter names are irregular words that are not in word-segmentation format but link-writing or abbreviation, cleaning work is necessary to make the parameter in a better form for semantic annotation.
Algorithm1: obtainParameterAnnotation Input: PC: a parameter concept of the Web service. OD: the operation description of PC. Output: OC: the ontology concept annotated PC. 1 OC ՚ 2 if getLocalWordList(PC) == true 3 OC ՚ getOCFromWordList(PC) 4 return OC 5 PC ՚ getCleanParemeter (PC) 6 ՚ getAnnotationFromSpotlight (PC, OD) 7 if OC == 8 OC ՚ getOntologyConcept(instanceURI) 9 storeIntoLocalWordList (PC, OC) 10 return OC
Algorithm2: obtainWebServiceAnnotation Input: WS: Web service contains a set of operations {OP1,OP2,…,OPn} ,while one operation contains a set of parameters {PC1,PC2,…,PCn}. Output: AWS: the Web service with annotation. 1 AWS ՚ WS 2 for each OPi אWS do 3 for each PCi אOPi do 4 OD ՚ getOperationDescription(PCi) 5 if isComplexType(PCi) == true 6 for each subpc אPCi do 7 OC ՚ obtainParameterAnnotation(subpc, OD) 8 AWS ՚ AWS { subpc, OC} 9 else 10 OC ՚ obtainParameterAnnotation(PCi, OD) 11 AWS ՚ AWS { PCi, OC} 12 return AWS
After the process of parameter cleaning, the next steps of our approach involve instance matching and ontology mapping. These processes consist of taking each parameter concept of a Web service in WSDL and associating it to the appropriate concept in the DBpedia Ontology, with the assistance of the DBpedia Spotlight. During the whole process, the annotation vocabulary provides the previous annotation records which may be helpful when we deal with the parameter that we have annotated before. In turn, it can be enriched with the annotation procedure going on, making it a repository of Web Services and Linked Data.
1) Taking IO into consideration: Many inputs and outputs of the same service have the same name in the form of getAbyB, or the outputs are named in the form of getB. Obverously, A is the input keyword and B is the output keyword. E.g., GetWeatherByPlaceNameResult is an output of a service, we can extract the keyword weather. 2) Separation of words: On account of the definations of parameter names without white space, we speparate these words according to the uppercase of the first letters. This process is the major step for parameter cleaning. Previously, filtering the punctuation and dashes is necessary.
Figure 2. Process of semantic annotaion based on DBpedia.
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3) Filtration of meaningless words: After separation, some meaningless words should be neglected. A number of words that their appearance in WSDL is higher than 90% are meaningless owing to the lack of discrimination. The meaningless words are maintained in a table in our database. 4) Restoration of abbreviations: Finally we restore the abbreviations by comparing them to the abbreviation table also maintained in our database. The next critical step to semantic annotation is to derive the appropriate DBpedia instance data that match the parameter. The whole process is illustrated in Fig. 3. In our approach, by means of HttpClient, we can invoke the service of DBpedia Spotlight. By configuring the filtering parameters and taking parameter name which has been cleaned and its operation description as input text, Spotlight responds the content in XML format which contains DBpedia URI matching to the parameter keyword, as shown in Fig. 4. We can extract the DBpedia instance data with the help of Domj4. These data will be mapped to the DBpedia ontology concept by querying the DBpedia Ontology RDF type statements. A few DBpedia URIs cannot find the corresponding ontology concept because some of them are not classified in the ontology. Under such circumstances, with the help of DISCO [15], we compute the distributional similarity between the instance data and each ontology concept in the corpus token from the English Wikipedia, and choose the ontology concept which has the maximal similarity as the semantic annotation for the parameter.
It appears quite simple to get semantic annotation for the Web service after the annotation of the leaf node, which is shown in Algorithm 2. For a Web service, we can get all the operations, then achieve the parameters of each operation, if the parameter is a simple type concept, just follow the steps described in Algorithm 1, otherwise, decompose the parameter recursively. As a result, a Web service has been enriched with semantic annotations. IV.
A. Evaluation In order to evaluate the applicability and usefulness of the proposed method, we propose an evaluation framework to analyze the experimental results. The framework contains measures specific to our annotation approach. In our work, the annotation result can be defined as 6-tuple: ൌ൏ ܣǡ ܤǡ ܵǡ ܫǡ ܧǡ ܹ , where:
By following the above steps, the output of Algorithm 1 is the DBpedia Ontology attached to this parameter. E.g., a Web service named currencyService has an operation called getCurrencyList which can retrieve all available currencies, since this operation has an output named getCurrencyListReturn. By applying our annotation method, the keyword obtained after the processes of parameter refinement and cleaning is currency. The DBpedia instance URI and DBpedia Ontology concept that we achieve are http://dbpedia.org/resource/Currency and http://dbpedia.org/ontology/Currency respectively.
Web of Data
A is the set of Web service parameter concepts.
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B is the set of Web service parameter concepts which are attached with DBpedia instance data and ontology concepts after annotation.
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S is the set of Web service parameter concepts which are attached with accurate DBpedia instance data after annotation.
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I is the set of Web service parameter concepts which are attached with accurate ontology concepts after annotation.
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E is the set of Web service parameter concepts which are attached with similar and relevant DBpedia instance data or ontology concepts after annotation.
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W is the set of Web service parameter concepts which are attached with incorrect DBpedia instance data after annotation.
ൌ൏ ݎܣǡ ܴ݁ǡ ݉ܣ, where: Invoke
dom4j
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Therefore, it can be inferred that ൌ , and െ represents the set of all the Web service parameter concepts that cannot be annotated by our approach. The evaluation result for annotation can be defined as 3-tuple:
Parameter Name Operation Description
EVALUATION AND EXPERIMENTAL RESULTS
L
XML
Figure 3. Matching DBpedia instance data to a parameter.
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Ar is the annotation rate of the semantic annotation for Web services, while ൌ ȁ תȁ ോ ȁȁ.
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Re is the precision of the semantic annotation for Web services, while ൌ ȁሺ ሻ תȁ ോ ȁȁ.
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Am is the ambiguity of the semantic annotation for Web services, while ൌ ȁ תȁȀȁȁ.
Among the above evaluation value, Re is an important indicator for semantic annotation for Web services which reflects the validity and accuracy of the annotation approach. Meanwhile, Ar embodies the suitability and stability of the annotation algorithm of a certain dataset, and the value of Am indicates the capability of ambiguous annotation, although no precise semantic information has been attached
Figure 4. Example of the XML document responded by DBpedia Spotlight.
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DBpedia Ontology, this is also the main reason why the parameters of complex type have higher precision than the simple type in the results of Travel300. And services in OWLS-TC4 contain relatively broader concepts. On the other hand, names of some leaf elements of Travel300 cannot express their functions, with unknown abbreviation, while names of OWLS-TC4 are defined relatively more normative and meaningful.
to the service, the evaluation argument Am can be used for fuzzy query or recommendation for Web services. B. Experimental results The experimental environment is Windows 7 equipped with 3GB memory, Myeclipse 9, Mysql 5.5, and DBpedia 3.7 downloaded by March 24, 2012. We perform our annotation on two datasets as shown in Table I. The first dataset includes of 300 services in WSDL taken from different repositories on the Web, relevant to the domain of travel and weather. We choose these services for the reason that we will make comparisons with the result of [7]. The second dataset incorporates OWLS-TC version 4 which consists of 9 different domains. From now on, we refer to the first and second dataset as Travel300 and OWLS-TC4 respectively, and refer to our approach and the annotation method in [7] as BLD and BDO respectively. At the same time, we process both datasets in a same way when applying BLD and only annotate Travel300 by BDO.
Travel300
OWLS-TC4
# services
300
1090
# interfaces
1897
1090
# parameters
27568
3175
TABLE II.
simple type parameters
complex type parameters
Ar
98.93%
99.64%
100%
Re
42.06%
76.61%
79.87%
Am
38.27%
17.30%
13.67%
TABLE IV. type
complex type parameters
parameters
|A|
20440
7128
3175
|B|
20222
7102
3175
|S|
5557
4505
1613
|I|
2949
936
923
|E|
7739
1229
434
|W|
3977
432
205
Complex type parameters
# parameters
7128
20440
ȁ ȁ
5773
18171
81.0%
88.9%
Fig. 5 shows the distributions of parameters which are annotated incorrectly in interfaces by our method BLD. From Fig. 5 (a), we can see that more than 1/3 of the interfaces are annotated properly, and ca. 83.03% of the interfaces have no more than 3 mislabeled parameters. Among the remaining less than 2/3 of the interfaces which are annotated incorrectly, there are ca. 41.74% of the interfaces only have one mislabeled parameter. Furthermore, with the number of mislabeled parameters increasing, the less the corresponding interfaces it reveals. Fig. 5(b) suggests that OWLS-TC4 has the better results, with 83.85% of right interface annotation. As a result, we can draw the conclusion that for most of services, our method produces a high annotation rate with great accuracy.
OWLS-TC4
simple type parameters
RESULTS OF BDO FOR TRAVEL300
Simple type parameters
Re+Am
RESULTS OF BLD
Travel300 Set type
OWLS-TC4
Travel300 value
DETAILS OF THE EXPERIMENTAL DATASETS
Name
EVALUATION RESULTS OF BLD
# Interface
By applying our approach to both datasets, we can gain the annotation results. The elements in RS are displayed in Table II. Moreover, we have obtained the measurements of EV through calculation as shown in Table III. These results reveal that our annotation approach has a very high rate of annotation with 100% annotation rate for OWLS-TC4. As to Travel300, there is higher ambiguity and the less accuracy for parameters of simple type, while for the complex type, with higher accuracy, the annotation results turn out to be considerable. Our approach is more effective for OWLS-TC4 with higher precision. The reason why the results of OWLS-TC4 are better than those of Travel300 is probably that, on one hand, the DBpedia Ontology is a cross-domain ontology which defines the concepts more generally than the domain ontology, so some specific concepts, especially some parameters of simple type, can hardly find the precisely corresponding concepts in the
1000
1000
100
100
# Interface
TABLE I.
TABLE III.
10
155
17 10 3
1
1
1
10 # Mislabeled parameters
100
1 1
10 # Mislabeled parameters
(a) (b) Figure 5. Distributions of parameters which are annotated incorrectly in interfaces by BLD: (a) Distributions of mislabeled parameters in 1897 interfaces of Travel300; (b) Distributions of mislabeled parameters in 1090 interfaces of OWLS-TC4.
As to Travel300, we conduct another experiment by applying BDO, and its annotation results are shown in Table
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data resources. Moreover, there is a need to present an automated evaluation method for large-scale semantic Web services annotation results. We also intend to perform our approach on a large scale of Web services, which can give rise to the semantic network for Web services on the Web of Data, enabling a significant revolution in how the services are accessed and utilized.
IV. It can be seen that our method results are close to but slightly lower than BDO. Fig. 6 illustrates the elements of EV for comparison. The annotation rate of our method is lower than BDO for the reason that the domain ontology of BDO is a “tailor-made” ontology which is manually built with help of domain expert, and during the annotation process, BDO is with the aid of human intervention while BLD is automated. As to the results of the parameters of complex type, BLD has lower error rate, the reason is the same with that the results of complex type parameters are better than the simple type, as mentioned above, the DBpedia Ontology contains more general concepts to match the parameters of complex type. Compared to BDO, our proposed method has natural advantages such as no domain restrictions, more automated with less human involvement, and greater flexibility to optimize. 100.00%
ACKNOWLEDGMENT Funded by NSFC grant No.61173155 and No.61070202, and the 985 Project of Tianjin University grant 2010XG-0009. Corresponding author:
[email protected]. REFERENCES [1] [2]
100.00% BLD
95.00%
BDO
95.00%
90.00%
90.00%
85.00%
85.00%
80.00%
80.00%
75.00%
75.00%
[3]
[4]
70.00%
70.00% Ar
Re+Am
Ar
Re+Am
[5] (a) (b) Figure 6. Comparison of BLD and BDO: (a) Parameters of simple type; (b) Parameters of complex type. [6]
From the experimental results, we can draw a conclusion that our proposed approach is effective and feasible, with high annotation rate. In addition to that, it can be performed on a wide range of Web services in broad areas, and lay a solid foundation for service composition and analysis of the semantic service network. Due to the general concepts in the DBpedia Ontology, our approach can be also of value for navigation and recommendation for Web services. V.
[7]
[8] [9]
CONCLUSION AND FURTURE WORKS
In this paper, we have put forward an automated approach to semantic annotation for Web services based on the DBpedia knowledge base which is available as Linked Data. Specifically, the main work in our paper addresses two major aspects. One is a novel semantic annotation method for the inputs and outputs of Web services by making use of the DBpedia dataset and DBpedia Spotlight, and the other is the evaluation of our approach performed on two different datasets. Our experimental results have shown that our approach have led to an effective and feasible service annotation. As a consequence, our proposed annotation approach has unearthed the rich semantics on the Web of Data to Web services. In turn, it opens new perspectives for the future semantic Web services. Meanwhile, it can lay a solid foundation for services discovery and automated service composition.
[10]
[11]
[12]
[13]
[14]
[15]
In the future, we will extend our approach to more Linked Data resources and ontologies for the sake of the abundant interlinking relations between DBpedia and other
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