On the Transformation of Sentences with Genitive

On the Transformation of Sentences with Genitive Relations to SQL Queries Zsolt T. Kardkov´ acs Budapest University of Technology, Department of Telecommunications and Mediainformatics [email protected]

Abstract. In our ongoing project called “In the Web of Words” (WoW) we aimed to create a complex search interface that incorporates a deep web search engine module based on a Hungarian question processor. One of the most crucial part of the system was the transformation of genitive relations to adequate SQL queries, since e.g. questions begin with “Who” and “What” mostly contain such a relation. The genitive relation is one of the most complex semantic structures, since it could express wide range of different connection types between entities, even in a single language. Thus, transformation of its syntactic form to a formal computer language is far from clear. In the last decade, several natural language database interfaces (NLIDBs) have been proposed, however, a detailed or a general description of this problem is still missing in the literature. In this paper, we describe how to translate genitive phrases into SQL queries in general, i.e. we omit Hungarian-dependent optimizations.

1

Introduction

Deep web sites (e.g. library and news portals, book stores, theatre and movie guides) are usually based on data stores. Contents of these sites are rarely visible by search engines despite of the fact that they are publicly accessible on the internet since they are often dynamically generated by forms or by dynamic parameters in the address line. Information stored in these databases contain about four hundred times more data than are searchable nowadays by search engines[1]. In addition, databases are yet structured and categorized and so they could provide a much more accurate answer to any user request. The main question is how to query these sites, in general? In the project WoW, we aimed at creating a natural language interface, mainly focus on Hungarian, to query the deep web. To achieve this goal one has to transform interrogative sentences into a formalized language, for example into XQuery, XPath or simply into SQL. Later in this paper, we focus only on the SQL as a target language, however, our results can be easily adopted for both XQuery and XPath according to [2]. One may ask why we need natural language (interface) to query the deep web. Are not keyword based engines and form or menu driven interfaces enough? A. Montoyo et al. (Eds.): NLDB 2005, LNCS 3513, pp. 10–20, 2005. c Springer-Verlag Berlin Heidelberg 2005

On the Transformation of Sentences with Genitive Relations to SQL Queries

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Creating database queries from keywords (a set of words) instead of sentences would result in a remarkably worse precision and recall rate. For example, keywords “president”, “Russia”, “visit” and “US” could mean – – – –

“When did the president of Russia visit the US?”, “Which president visited Russia and the US?”, “Which president of the US visited Russia?”, “Who can visit the president of Russia from the US?”, etc.

In these examples, all alternative sentences affect different database queries and thus many irrelevant answers, too, since the user asked only one of them. Solutions that do not deal with the given morphosyntax of the sentence, e.g. AnswerBus[3], Askjeeves[4], BrightPlanet[5], Ionaut[6], also suffer from this problem, even if input question contains a genitive phrase. (To test whether the solution is keyword based or not – even if the input is a natural language question – try: “What is the name of the son of Juan Carlos I?”. The results usually contain a wide variety of person names whose sons’ name is Juan Carlos or details of the life on the present King of Spain which includes or not his son’s name.) Form based or other graphical interfaces are the best choices to retrieve information from a single database or to query web sites on a concrete topic. On the one hand, there are differences in structural design and in granularity of data between databases in a multi-database environment. That is why a form based search application needs to semantically restructuring user input according to the target databases or it needs to reduce differences in a way. Both of them are hard tasks if the number of databases is not strongly limited or if there is no agreement between the site owners. On the other hand, the number of attributes to be queried is not bounded in search engines. Without a hard limitation on the number of the topics, form based applications become unusable or impractical. In addition, natural questions as above can not be displayed by forms easily. For a more detailed analysis on the differences between NLIDBs, keyword and menu based search engines, see [5, 7, 8]. From our point of view deep web can be treated as a most universal database which has no definite data structure (or it is not known) and thus our project produces a natural language interface to databases (NLIDBs). On the other hand, deep web could serve as a knowledge base or as a source of knowledge for question answering systems (QAS). The only difference between deep web engines and QAS is that deep web engines retrieve sites with appropriate knowledge to answer questions instead of finding answers for them. That is, deep web querying incorporates some aspect of both NLIDBs and QAS. There has been a lot of work in recent years on both natural language processing and web-based queries. A non-exhaustive list of projects: Practice[9, 10], START[11–13], SQ-HAL[14], NL for Cindi[15], Masque/SQL[16], Chat-80[17] or Team[18] provide solutions for English, Spanish NLQ[19], Sylvia-NLQ[20] for Spanish, Phoenix[21] for Swedish, Edite[22], LIL/SQL[23] for Portuguese, NChiql[24] for Chinese and KID[25, 26] for Korean languages. Although several ideas and techniques have been adapted from these systems to WoW, none of

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Zsolt T. Kardkov´ acs

them contains a general and clear description on how to transform correctly genitive relations to their formal languages. In this paper, we focus on this problem and propose a possible solution. Our work differs from others in several ways. Firstly, we state new theoretical foundations to deal with non-trivial genitive phrases. (We call a genitive phrase non-trivial if its SQL equivalent contains at least one embedded query.) Our solution is not limited to giving a formal semantics for genitive relations but includes transformation methods from syntactically analyzed sentences to SQL statements. Last but not least, our approach can also handle compound (or multiple) genitive phrases which is beyond the capabilities of above mentioned projects. The solution is designed for our native language, however, it is language independent in its own, and we omit language-dependent optimizations. The paper is organized as follows: in the next section we introduce basic notions. We state main problems and a new algorithm based on the design of targeted databases in Section 3. Section 4 shows how our algorithm works. We summarize our results in the last section.

2

Preliminaries

Genitive relations could express a wide range of different things in natural languages, see e.g. [27–29], which emerges problems on natural language processing as a new source of ambiguity. For example, – – – –

“head of the firm” (affiliation or group membership), “the author of Hamlet” (attribution), “Edith’s TV” (ownership), “Bizet’s Carmen” (authorship),

etc. refer different relationships between entities, however, their syntactical form (and thus their syntactical decompositions) is the same both in Hungarian and in English. This also means that after the syntactical analysis one can determine just semantically which aggregation or reference in databases corresponds to the relationship encoded in the given expression. Identification and syntactical decomposition of genitive phrase elements are language dependent tasks which were widely discussed earlier for English in [27, 29, 30] and for Hungarian [31]. After identifying possessors and possessums of a sentence one needs to determine what genitive relations express, “mean” or refer to, and how to transform them to query databases. Obviously, the meaning of an expression depends on the terms used in it. The Table 1 illustrates by simple and similar examples that the transformation is far from trivial. The term “Mecca of movies” needs some further explanations. In the one hand, it refers in Hungarian to a city visualized usually in movies which can be a bit different from the reality. On the other hand, it also could mean (as an idiomatic expression) a location where movie shooters return to over and over like Islamites return to Mecca. Unfortunately, neither of these senses is fully captured by our methods since it assumes a common human knowledge and associative (metaphoric) connections between entities.


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Table 1. Genitive phrases and one of their equivalent SQL statements Bizet’s Carmen

SELECT title FROM Operas WHERE author = ’Bizet’ AND title = ’Carmen’ dramas of Shakespeare SELECT title FROM Dramas WHERE author = ’Shakespeare’ Tom’s name SELECT name FROM Persons WHERE name = ’Tom’ Mecca of movies SELECT shootingsite FROM Movies GROUP BY shootingsite HAVING COUNT(*) >= ALL (SELECT COUNT(*) FROM Movies GROUP BY shootingsite) book’s characters SELECT character FROM Roles WHERE play IN ( SELECT title FROM Books ) head of department SELECT head FROM Departments king of Spain’s name SELECT name FROM Kings WHERE name IN (SELECT king FROM Reigns WHERE kingdom = ’Spain’)

On translating interrogative sentences into SQL queries one has to find a mapping between the elements of database design and the ontological concepts or else SQL queries will not fit the database structure. This implies that a transformation of the ontological taxonomy needs to be represented in the database design and conversely the elements of the database structure are the basis of the ontological taxonomy. One may ask then: does the knowledge stored in the database design suffice to achieve the decomposition of genitive relations? Let X ⇒ Y stand for a genitive relation in which X is the possessor part and Y is the possessum part. According to relational database terms, genitive expressions can consist of values (of attributes), individuals (tuples, entities and instances), schemas (sets, classes) and attributes (properties). Let us denote them by V, I, S and A, respectively. Working implementations, e.g. [9, 10, 12, 21, 22], say yes to that question, hence their knowledge for semantic analysis is originated from the database design. If data are modeled by triples (was proposed by [13, 32]) then they can only query for elements of these triples, which is a quite restricted form of the genitive relations. That is, these solutions only handle genitive relations of the form S ⇒ A and I ⇒ S, but they can not deal with multiple or compound genitive relations, e.g. “king of Spain’s name”. Before introducing a more general semantics for natural genitives we need some clarification in notions and introduction of some new terms. Definition 1 (Natural keys). Let DB = be a database, where V, I, S and A are non-empty sets of stored attribute values, individuals, schemas and attributes, respectively. Let κ : S → A be a function that maps every schema s ∈ S to an attribute α ∈ A in s such that any individual ω ∈ I of s is named in natural languages by the value v ∈ V of α in ω. We say κ(s) is the natural key of s.

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For example, κ(book) = title, κ(person) = name, etc. Natural keys and wellknown (primary) keys differ in that the former ones could have the same value for distinct entities in a schema, while the latter ones are unique by definition. Definition 2 (Reference function). Let DB = be a database and α ∈ A be an attribute of a schema s ∈ S. Assume that attribute names appear in at most one schema (unique property name assumption). The function ϕ : A → S is called reference function and it is defined as follows: s if α = κ(s) ϕ(α) = s’ if α = κ(s), for some s’ ∈ S The function ϕ is well-founded iff for any value of α is also value of ϕ(α). We also define the inverse function ϕ−1 (s) = {α | ϕ(α) = s}. Values of natural keys are treated as constants, i.e. they are references to themselves. Individuals are usually distinguished from each other in databases by a unique id. If there is a reference function in a database then unique ids have to be references, since they are not natural at any sense. As a consequence, databases with reference function must have a schema for unique ids or else V = I. These options are equivalent with respected to the genitive relations and make no real distinctions between values, references and individuals. Later on this paper, we call this construction (V)ISA-model of genitive phrases, or shortly (V)ISA-model, that is, any database with a reference function is a (V)ISA-model. Proposition 1. Let DB = be a (V)ISA-model with a reference function ϕ. ϕ defines an equivalence relation on A. Further on this paper, α stands for the equivalence class of an attribute α. The proof is pretty straightforward. It is also easy to see that every equivalence class contains only one natural key. Note that, reference function can be used for navigation between elements of relations, since ϕ is also a constraint that determines which attributes can be joined (in a semantically valid way). Definition 3 (Weak well-foundedness of genitive relations). Let DB = be a (V)ISA-model with a reference function ϕ. The genitive relation X ⇒ Y is called weakly well-founded iff – – – – – –

X X X X X X

⊆ I and Y ∈ I (e.g. “Bizet’s Carmen”) ⊆ I and Y ∈ S (e.g. “dramas of Shakespeare”) ⊆ I and Y ∈ A (e.g. “Tom’s name”) ⊆ S and Y ∈ I (e.g. “Mecca of movies”) ⊆ S and Y ∈ S (e.g. “book’s characters”) ⊆ S and Y ∈ A (e.g. “head of department”)

Weakly well-founded genitive relations are the most common genitive phrases in natural languages. Usually, attributes or attributive descriptions can not stand


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for possessors, hence their functionality is quite different – they can not have ownership, partitive, constitutional, membership, etc. relations[27] with some other element. Pragmatically speaking, to be a schema means to be described by parameters, measures, qualities or values; to be an attribute means to belong to some schema and to have some value; and at last to be an individual is to be a named element of a more abstract category or schema. In a genitive phrase X ⇒ Y , if X is a schema (schema name) then usually it has a general meaning. Formally, schema (as a set) is equivalent to the set of entities (values of natural keys) belong to it, however, in natural languages it not necessarily means the same. For example, “head of department” is not the head of all but one of known departments (department names); while in the case of “Mecca of movies” are not the Mecca of all and not any of known movies (movie titles). That is, schemas can not be modeled as sets in general. Actually, schemas can be individuals from the designer’s (or the speaker) point of view and vice versa. In other words, we treat schema as a more abstract individual from some others, and thus, we can create a hierarchy between individuals based on abstraction. As a consequence, we have S ⊆ I. Definition 4 (Strong well-foundedness of genitive relations). A weakly well-founded genitive relation X ⇒ Y is called strongly well-founded or wellfounded iff X ⊆ I.

3

On the Semantics of Genitive Relations

It is easy to see according to the previous contexture that weak and strong wellfoundedness are not necessarily different terms, it is not a real limitation on the expressive power. While well-foundation determines what kind of element can be a possessor or a possessum, it does not deal with the real semantics. We need to introduce what valid “ownership” could mean based on the works of [33, 34]. Definition 5 (Valid genitive relations). Let a relation Π : A × A be defined on a DB = (V)ISA-model. For α, β ∈ A and there exists a s ∈ S they are both belong to then Π(α, β) is true if and only if values of α can be possessors in well-founded genitive phrases for which β or values of β are the possessum. For example, Π(“person’s name”, “date of birth”) is a valid possessive relation but in reverse is not. Also note that, Π is a language-dependent relation, hence it raises semantic issues and as such it is the semantic constraint proposed in [27, 34]. There are some more thematic roles, e.g. in English than are in Hungarian[27, 28]: – – – –

“ring of gold” (materialization or constitution) “Mr. Jones of Suffolk County” (origination or affiliation) “the herd of cattle” (natural measure) “man of yesterday” (temporal predication)

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Their special meanings are not fully captured by our proposal. Notwithstanding, one can represent these connections in a modified database model extending Π (e.g. in the case of “ring of gold”) or one needs to treat them as idioms (e.g. “man of yesterday”) and processed in an alternative way. In databases, Π can be stored as a schema with two attributes and Π is true for all tuples which are defined in the relation. Definition 6 (Semantics of genitive relations in (V)ISA-model). Let DB = be a (V)ISA-model with a reference function ϕ. We introduce the following functions and notions: • We denote by α ∈ s for some s ∈ S the fact that s has an attribute named α. • Let Σ : 2A → 2S be a function that maps a set of attributes onto a set of such schemas that contain at least one element of the attribute set. • The σ : I → 2A is a function that maps any individual i ∈ I onto a set of attributes with values i. That is, σ defines the set of attributes where an individual i may appear as a value. 1. if Y ∈ I then let γ := σ(Y ) 2. if Y ∈ S then let γ := ϕ(κ(Y )) 3. if Y ∈ A then let γ := Y If there exist attributes α ∈ σ(X), β ∈ γ such that ∃s ∈ Σ(X) ∩ Σ(γ), α, β ∈ s and Π(α, β) hold then X ⇒ Y is the set of values (individuals) of β assuming that X is a single individual. If X stands for a set of individuals then X ⇒Y = χ ⇒ Y. χ∈X

Note that the semantics does not depend explicitly on the database design; hence there is no mention of requirements for world modeling. In other words, attributes α, β and the container schema s are free variables; their concrete names or values depend on the site we are trying to query only. As a consequence, the algorithm is quite effective in the sense it has to find a proper triple for a quite limited number of attributes and schemas. Such a constraint resolution can be very effective by constraint logic programming (Prolog). On the other hand, it needs transformations for each site we want to search in, however, every translation can be done and be supervised by these sites. The novelty of this approach based on this fact: it takes into account that data can be separately stored but there can be a well-defined path or a path expression to navigate from an entity to another from a natural point of view. If this last condition does not hold it can not be resolved the association even for a human. Obviously, if natural keys served for navigating are replaced by commonly used primary keys then most of our algorithm still works. Notwithstanding, that would be a less precise solution since it would produce a large amount of false positive results (consider e.g. dates or numbers).


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Algorithm 1 ((V)ISA-algorithm) The following algorithm transforms a genitive phrase of the form X ⇒ Y into an equivalent database (site) specific SQL code. Let D denote the given database. We use notions of the Definition 6. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

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determine γ according to Definition 6 find appropriate α, β and s due to Definition 6 in D if Y is an individual then $insert := "β = Y AND " else $insert := "" if X is a single individual then SELECT β FROM s WHERE $insert α = X else { if X is a schema then replace it by SELECT κ(X) FROM X if X is a genitive phrase then { apply (V)ISA-algorithm for it and replace X by the result of the algorithm } SELECT β FROM s WHERE $insert α IN ( X ) }

(V)ISA-Algorithm by Examples

Consider e.g. the expression “Bizet’s Carmen”. There exists an attribute α = “author” and β = “title” in “Operas” ∈ Σ(σ(“Bizet”)) ∩ Σ(“Carmen”) for which Π(“author”, “title”) also holds. Since “Carmen” is an individual, $insert contains "title = ’Carmen’". That is, the solution is: SELECT title FROM Operas WHERE title = ’Carmen’ AND author = ’Bizet’ which is exactly the same as in Table 1. It is easy to see, that for the expression “head of department” (V)ISAalgorithm produces SELECT name FROM Departments WHERE name IN ( SELECT name FROM Departments ) which is an equivalent to the solution seen in Table 1. Unfortunately, this solution is not equivalent to the original genitive expression as we pointed out earlier. Notwithstanding, there can be no better solution, thus this defectiveness is rather originated from the database formalism or data model than from the algorithm. The algorithm also works for multiple or compound genitives. For example, let us examine the phrase “king of Spain’s name” ((“Spain” ⇒ “king”) ⇒ “name”). For “Spain” ⇒ “king” α = “kingdom”, β = “king”, s = “Reigns”. After that one has to consider all possible individuals of “Reigns” by definition. As a consequence, algorithm generate identical solution to which was seen in Table 1. The algorithm generates appropriate SQL statements for schema-reflexive multiple genitive relations due to the definition of Π. Consider, e.g. the question

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“Who is the husband of Tom’s wife?”). The genitive phrase “Tom” ⇒ “wife” ⇒ “husband” is transformed by (V)ISA-algorithm to: SELECT husband FROM Consorts WHERE wife IN ( SELECT wife FROM Consorts WHERE husband = ’Tom’ ) since Π(“husband”, “wife”) and Π(“wife”, “husband”) are valid in the schema “Consorts” while e.g. Π(“husband”, “husband”) is not. Our algorithm is not universal (or fully semantic) in the sense that it neither handles metaphoric or common human knowledge based expression (e.g. “Mecca of movies”) nor idiomatic expressions (e.g. “man of yesterday”). Moreover, it also fails for derivatives of complex verbs, e.g. for “winner”. The problem is such derivatives could have different representations based on the local context, e.g. win a match could mean scoring more goals, getting less error points, being faster than others or win three games in a row. These notions can be captured by ontologies but integration of them into this model is far from trivial, we are still working on it.

5

Conclusions

Algorithms or foundations on the decomposition of possessives are missing from the literature. In this paper, we stated problems and we proposed solutions which deal with genitive phrases in natural language processing and translate them properly into SQL queries. Our approach is universal in the sense that it works for all kinds of genitive phrases and also handles compound structures which is one of the most important novelties of this approach. Obviously, it has limitations. Our algorithm can not resolve expressions with wider or metaphoric sense, concetti, idiomatic expressions, terms which assume deeper human knowledge and derivatives of predicate verbs. Our proposal does not require ontology but with additional information on reference functions, natural naming conventions and valid possessive relations one can determine unambiguously the possessor of genitive phrases assuming that the predicate verb has no special meaning in the sentence. We also demonstrated by examples how our proposition works. Unfortunately, there still not exists a query corpus with genitive phrases for Hungarian and are rarely available even for English. That is, we could not present a detailed comparison to other solutions, however, our corpus with 87 genitive phrases has been properly processed in 87,3% (11 wrong solutions). We are working on building a reference corpus with both language dependent and independent parts.

Acknowledgement The research was partially funded by the Hungarian Research and Development Foundation under the name “Szavak h´ al´ oj´ aban” (NKFP-2002/19) and “Magyar


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egységes ontológia” (NKFP-042/04). The project was also funded by Axelero Internet Ltd. (T–Mobile) the largest Hungarian Web Service Provider.

References 1. 2. 3. 4. 5. 6. 7.

8.

9.

10.

11. 12. 13. 14. 15.

16.

17. 18.

19.

(Brightplanet – deep web white paper) http://www.brightplanet.com/pdf/. (Iso/iec 9075-14:2003 standard) http://www.iso.org. (Answerbus) http://www.answerbus.com/. (Askjeeves) http://www.askjeeves.com/. (White paper on deep content retrieval) http://www.brightplanet.com/pdf/. (Ionaut) http://www.ionaut.com:8400/. Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases – an introduction. Journal of Natural Language Engineering 1 (1995) 29–81 Winkler, H. In: Suchmaschinen – Metamedien im Internet? Campus Verlag, Frankfurt/New York (1997) 185–202 In English: http://www.unipadernborn.de/~timwinkler/schum\_e.html. Popescu, A.M., Etzioni, O., Kautz, H.: Towards a theory of natural language interfaces to databases. In Johnson, W., André, E., Domingue, J., eds.: Proc. of the 8th IUI 2003. Internation Conferences on Intelligent User Interfaces, Miami, ACM Press (2003) 149–157 Popescu, A.M., Armanasu, A., Etzioni, O., Ko, D., Yates, A.: Modern natural language interfaces to databases: Composing statistical parsing with semantic tractability. In: Proc. of COLING 2004. International Conference on Computational Linguistics, Geneva, Switzerland (2004) Katz, B. In: Using English for Indexing and Retrieving. Volume 1 of Artificial Intelligence at MIT: Expanding Frontiers. MIT Press (1990) 134–165 (Start) http://www.ai.mit.edu/projects/infolab/. Katz, B., Lin, J.L.: Start and beyond. In: Proc. of SCI 2002. Volume XVI of World Multiconference on Systemics, Cybernetics and Informatics. (2002) (Sqhal) http://www.csse.monash.edu.au/hons/projects/2000/Supun.Ruwanpura. Stratica, N., Kosseim, L., Desai, B.C.: A natural language processor for querying cindi. In Milutinovic, V., ed.: Proc. of SSGRR 2002. International Conference on Advances in Infrastructure for e-Business, e-Education, e-Science, and e-Medicine on the Internet, L’Aquila, Italy, Electronically: http://www.ssgrr.it/ (2002) Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Masque/sql – an efficient and portable natural language query interface for relational databases. In Chung, P., Lovegrove, G., Ali, M., eds.: Proc. of IEA/AIE 93 Conference. IEA/AIE Conferences, Edinburgh, Gordon and Breach Publishing (1993) 327–330 Warren, D.H., Pereira, F.C.: An efficient easily adaptable system for interpreting natural language queries. Computational Linguistics 8 (1982) 110–122 Grosz, B.J., Appelt, D.E., Martin, P.A., Pereira, F.C.: Team: An experiment in the design of transportable natural-language interfaces. Artifical Intelligence 32 (1987) 173–243 Rangel, R.A.P., Gelbukh, A.F., Barbosa, J.J.G., Ruiz, E.A., Mej´ıa, A.M., S´ anchez, A.P.D.: Spanish natural language interface for a relational database querying system. In Sojka, P., Kopecek, I., Pala, K., eds.: Proc. of TSD 2002. Volume 2448 of Lecture Notes in Computer Science., Brno, Czech Republic, Springer-Verlag (2002) 123–130

20


20. (Sylvia) http://www.lllf.uam.es/proyectos/sylvia.html. 21. Cedermark, P.: Swedish noun and adjective morphology in a natural language interface to databases. Master thesis, Uppsala University, Department of Linguistics (2003) 22. Reis, P., Matias, J., Mamede, N.: Edite: A natural language interface to databases – a new perspective for an old approach. In: Proc. of ENTER’97. Information and Communication Technologies in Tourism, Edinburgh, Springer-Verlag (1997) 317–326 23. Filipe, P.P., Mamede, N.J.: Databases and natural language interfaces. In Delgado, C., Marcos, E., Corral, J.M.M., eds.: Proc. of 5th JISBD. Jornada de Engenharia de Software e Bases de Dados, Valladolid, Universidad de Valladolid (2000) 321–332 24. Meng, X., Wang, S.: Overview of a chinese natural language interface to databases: Nchiql. International Journal of Computer Processing of Oriental Languages 14 (2001) 213–232 25. Chae, J., Lee, S.: Frame-based decomposition method for korean natural language query processing. International Journal of Computer Processing of Oriental Languages 11 (1998) 213–232 26. Lee, H., Park, J.C.: Interpretation of natural language queries for relational database access with combinatory categorial grammar. International Journal of Computer Processing of Oriental Languages 15 (2002) 281–303 27. Barker, C., Dowty, D.R.: Non-verbal thematic proto-roles. In Schafer, A., ed.: Proc. of NELS 23 Conference. North-Eastern Linguistics Conferences, Amherst, Massachusetts, GLSA Publications (1992) 49–62 28. Chisarik, E., Payne, J.: Modelling possessor constructions in lfg: English and hungarian. In Butt, M., King, T., eds.: Proc. of the LFG01 Conference. International Lexical-Functional Grammar Conferences, Hongkong, Stanford CSLI Publications (2001) 49–62 29. Jensen, P.A., Vikner, C.: The english prenominal genitive and lexical semantics. Workshop on the Semantics/Syntax of Possessive Constructions, Amherst, Massachusetts (2002) Invited paper. 30. Storto, G.: Possessives in context. Possessives and Beyond: Semantics and Syntax, Amherst, Massachusetts, GLSA Publications (2004) 59–86 31. Tikk, D., Kardkov´ acs, Z.T., Andriska, Z., Magyar, G., Babarczy, A., Szakad´ at, I.: Natural language question processing for hungarian deep web searcher. In Elmenreich, W., Haidinger, W., Machado, J.T., eds.: Proc. of ICCC 2004. IEEE Internation Conference on Computational Cybernetics, Wien, Austria (2004) 303–309 32. Katz, B., Felshin, S., Yuret, D., Ibrahim, A., Lin, J.J., Marton, G., McFarland, A.J., Temelkuran, B.: Omnibase: A uniform access to heterogeneous data for question answering. In Andersson, B., Bergholtz, M., Johannesson, P., eds.: Proc. of NLDB 2002. Volume 2553 of Lecture Notes in Computer Science., Stockholm, SpringerVerlag (2002) 230–234 33. Barker, C.: Possessive Descriptions. PhD thesis, University of Carolina, Santa Cruz, Department of Linguistics (1995) 34. Storto, G.: Possessives in Context – Issues in the Semantics of Possessive Constructions. PhD thesis, University of California, Los Angeles, Linguistics (2003)