Correspondence Patterns for Ontology Alignment

Transcription

1 Correspondence Patterns for Ontology Alignment François Scharffe and Dieter Fensel University of Innsbruck, Austria Abstract. We introduce in this paper correspondence patterns as templates to model ontology alignments. Correspondence patterns capture regularities recurring when aligning ontologies. They come in complement of ontology matching algorithms and graphical mapping tools, and facilitate the task of the engineer building the alignment between a pair of ontologies. We develop an ontology mediation framework based on three ontology correspondence abstraction levels. We particularly detail the most abstract level: correspondence patterns. 1 Introduction The semantic web contains many ontologies and is expected to contain more of them as it will develop. When two ontologies overlap, they can be linked together in order to enable exchange of their underlying knowledge. Such a link is called an alignment. An alignment between two ontologies specifies a set of correspondences, and each correspondence forms a bridge between a set of ontology entities. Finding alignments is a tedious task. There are many systems for doing so, but the quality of delivered alignments is not very high. Higher quality could be achieved by manually designing alignments but this is very demanding. Tools such as graphical user interfaces can help manual alignment design but they are not sufficient. We propose to help alignment designers by providing them with patterns of the most common correspondences so that building alignments can in most cases be reduced to instantiating patterns. We first explain what is meant by a correspondence pattern and how it can be instantiated and grounded in an executable language. Then we propose a language for expressing correspondence patterns based on existing expressive alignment languages. Finally we present our implementation of correspondence patterns. Most ontology matching systems [1] are limited to detecting simple equivalence or subsumption correspondences between single entities, and research concentrates on improving their quality on various datasets more than on finding more complex matches 1. This can be explained as the problem of detecting complex matches is not a trivial one. However, simple correspondences are often not sufficient to correctly represent the relationship between the aligned entities. 1 See the Ontology Alignment Evaluation Initiative 2007 evaluation criteria: A. Gangemi and J. Euzenat (Eds.): EKAW 2008, LNAI 5268, pp , c Springer-Verlag Berlin Heidelberg 2008

2 84 F. Scharffe and D. Fensel Example 1. In the Wine Ontology 2, whose main class is Wine, the class BordeauxWine represents instances of a popular French wine made in the region around Bordeaux. In the Ontologie du Vin 3 a similar wine is expressed as an instance of Vinwith an attribute terroir 4 indicating the wine provenance. Matching systems are able to detect the two correspondences Wine Vinstating equivalence between two wines and BordeauxWine Vin stating that BordeauxWine is a narrower concept than Vin. In this case, a more precise correspondence would restrict the scope of Vin to only those instances having Bordeaux as value of the terroir attribute. This correspondence where a class scope is narrowed by the value of an attribute of this class can be generalized in an attribute value restriction pattern, as illustrated in Figure 1. For the remaining of this paper we will use the following representation of patterns: a gray box represents a pattern. Green boxes represent correspondences between a set of ontology entities that are part of the pattern. Dashed lines between green boxes represent dependencies between parts of a single, complex correspondence. Lines attached to green boxes represent the ontological entities taking part in the correspondence. An example instance is given for each entity (in lower case). Arrows between entities indicate subject, predicate and object as in RDF. Fig. 1. Attribute value restriction pattern The correspondence designed in this example can be used in various mediation tasks: for Query Rewriting: A query addressed to a source ontology o s is rewritten in a query in terms of a target ontology o t. In our example, querying BordeauxWine will result in also querying Vin whose terroir is Bordeaux. Using the SPARQL query language 5,theoriginal query: SELECT?X WHERE {?X rdf:type wine:bordeauxwine. } 2 The Wine Ontology is available at: 3 The Ontologie du Vin is available at: 4 Terroir is a French word for a particular agricultural region 5

3 Correspondence Patterns for Ontology Alignment 85 becomes SELECT?X WHERE {?X rdf:type vin:vin.?x vin:terroir Bordeaux. } for Instance Transformation: A set of instances referring to a source ontology o s is transformed into instances referring to a target ontology o t.inour example, BordeauxWines instances will be transformed into instances referring to Vin with the terroir attribute specified. It is possible to realize this task using the SPARQL Construct statement: CONSTRUCT {?X rdf:type vin:vin.?x vin:terroir Bordeaux.} WHERE {?X rdf:type wine:bordeauxwine.} for Ontology Merging: in this case, ontology axioms are derived to create a new ontology from two source ontologies. Using description logics syntax, the correspondence of our example is represented like the following: BordeauxWine Vin terroir.{bordeaux} As we can see, a single modeling pattern can result in many representations, depending on the mediation task to be performed and the knowledge representation formalism used to perform this task. Inspired from design patterns in software engineering, this paper introduces correspondence patterns as helpers that facilitate the ontology alignment process. They improve graphical tools by assisting the user when creating complex correspondences, and we believe they are a first step towards matching algorithms able to detect complex matches. We base the pattern representation on a correspondence representation formalism modeling correspondences at an abstract level. This allows to later on retrieve and use the correspondence when needed. This paper is structured as follows: in Section 2 we situate correspondence patterns in the ontology alignment representation field, and introduce the alignment representation formalism used to represent patterns. We distinguish generic patterns representing common ontology mismatches in Section 3.1 and domain specific patterns for database to ontology mappings and ontology evolution in Section 3.2. We finally compare our approach to related works Section 4. 2 Three Representation Levels We consider three possible levels of abstraction when representing correspondences between ontologies. At the ground level are grounded correspondences, as represented in a mediation system executing them. Classical representations of correspondences at this level are the logical formalism of the reasoner, or the programming language of the mediator system. For example transformations of instances from the class source of a source ontology o s to the class target of a target ontology o t are represented in the following SPARQL query:

4 86 F. Scharffe and D. Fensel CONSTRUCT {?X rdf:type o s :source } WHERE {?X rdf:type o t :target. } The query above is an example of grounded correspondence for the specific task of instance transformation. Other mediation tasks such as query rewriting or ontology merging might require different systems to be performed. However, the correspondence between related entities remains the same at more abstract level. We therefore consider a correspondence level, where correspondences are represented in an abstract format that can be grounded to the language of the mediator system associated to the mediation task to be realized. This allows to specify correspondences independently from the expressiveness of the underlying knowledge representation formalism of the ontologies. Correspondences at this level can be conveniently exchanged between graphical tools, matchers and mediators. Correspondences are typically expressed using a specific ontology alignment representation formalism such as the alignment ontology 6.Amodeltheoretic semantics for the alignment representation formalism modeled by the alignment ontology is given in [2]. A correspondence pattern is at the top level of abstraction of the ontology alignment representation stack. Correspondence patterns are essentially correspondences and sets of correspondences with unspecified entities. Similarly to software engineering patterns, they act as templates to help finding more precise correspondences than simply relating one entity to another. Example patterns are shown in Examples 1,2 and 3. A correspondence pattern template alowing to formally describe patterns was introduced in [3]. We propose a number of generic patterns answering common mismatches appearing when trying to relate ontologies, according to the description of ontology mismatches in [4] and on experience in research projects. The pattern library is organized in an OWL class hierarchy, based on the Alignment Ontology [5]. In practice, patterns are represented as specific kind of correspondences whose possible instantiations are restricted using OWL axioms. The class hierarchy automatically organizes patterns according to their specificity, and a more general version of a pattern is thus obtained by taking its parent in the class hierarchy. The patterns library is modeled based on the Alignment Ontology under the Cell class. The library contains 35 correspondence patterns at the time of writing. Properties corresponding to the pattern template elements are modeled as OWL annotation properties: patternalias, patternproblem, patterncontext, patternknownuses, patternresultingcontext and patternsolutiondescription. An rdf:comment may be added to give a rough description of the pattern. A pattern in the library is modeled as a class with restrictions on its possible instantiations and an example instance is given for each pattern. The deepest one goes into the pattern hierarchy, the more specific are the patterns, making retrieval of patterns easier as suggested in [6]. Besides patterns themselves, the library provides a set of generic ontological entities used to compose patterns. These entities serve as placeholders to be filled with concrete entity names when using the pattern to build a concrete correspondence. These generic entities provide 6

5 Correspondence Patterns for Ontology Alignment 87 a reference structure for modeling concrete entities in the same way patterns provide a reference structure for modeling concrete correspondences. For example, the ClassConditionInstance models a class with a condition restricting its scope. 3 Correspondence Patterns This section provides insights on the pattern library. After studying general purpose patterns, we look at database to ontology and ontology versioning correspondence patterns. 3.1 Generic Patterns Patterns at the root of the hierarchy are classified based on the type of entities they are to be applied to (Attribute, Class and Relation). Equivalent correspondence patterns model the most frequent kind of correspondence usually used to solve a terminological mismatch 7. Subsumption correspondence patterns also model common correspondence arising when relating entities, they typically solve a granularity mismatch 8. These two patterns are in the scope of most matching discovery algorithms. They often need to be refined in order to obtain correspondences reflecting more precisely the relation standing between the related entities. Conditional and transformation patterns can be used to refine simple correspondences. Patterns using conditions are used when the scope of one entities in an ontology needs to be narrowed to match the scope of an entity in the other ontology (scope mismatch). Patterns can contain a restriction on the scope of a class based on the occurrence, thevalue, orthetype of an attribute. The attribute value restriction pattern illustrated in Figure 1 is a typical example. Relations and properties scopes can be narrowed according to their domain and range (data values for properties). Transformation patterns are used when a transformation of a property value is needed to fit the corresponding property in the other ontology. Conditions and transformations are expressed in our framework as part of the correspondence they are related to (the dashed lines in the figures). Example 2. (conditional pattern) One the one side, the Description of a Project (DOAP) ontology 9 contains revision, i.e. version numbers of released versions of projects. On the other side, the Open Source Software Vocabulary 10 (OSSV) models the latest release of a project. Aligning both ontologies requires to aggregate version numbers in DOAP and take the maximum as the last release 7 Terminological mismatches involve all mismatches due to differences of naming between ontological entities. 8 Granularity mismatches happen when part of the domain is more fine grained in one ontology than in the the other

6 88 F. Scharffe and D. Fensel Fig. 2. Aggregation pattern in the OSSV. The aggregation pattern shown in Figure 2 models this kind of correspondence. Datatype conversion 11, unit transformation and currency conversion are among the possible transformations. Union and intersection patterns can be used to relate entities modeled at different granularity. Patterns relating heterogeneous entities are used when the structural choices made by the ontology engineer differ to model a similar domain. For example, a relation in one ontology is represented as a class in the other ontology (See [7]). Example 3. (Transformation pattern) The Friend of a Friend (FOAF) ontology 12 describes persons and groups. It models the name of a person as a property with a string value. The VCard ontology 13 models names as object properties (relations) which point to a name structure having first, family and additional name attributes. Relating both names suppose concatenating the three VCard name properties to construct the FOAF name. The Relation-Class-Attribute to Attribute pattern, illustrated Figure 3 captures this correspondence. To be complete, the pattern should precisely describe how each entity is aligned to the other. 3.2 Application-Specific Patterns There are several applications and domains where specific correspondence patterns exist, among them are relational database to ontology mapping and ontology versioning. Relating databases to ontologies is an important topic as a considerably large amount of data is already available in relational databases. Publishing this data on the semantic web requires to either convert data to RDF stores or to maintain it in the relational database while providing a mapping to an ontology. This second solution is often preferred as more flexible. In order to align databases and ontologies, we identify database entities according 11 Datatype conversions solve encoding mismatches

7 Correspondence Patterns for Ontology Alignment 89 Fig. 3. Relation-Class-Attribute to Attribute pattern to our alignment representation formalism: tables as Classes, table columns as Attributes, with the distinction between Properties for standard columns, and Relations for foreign key columns. Following the work in [8], we identify the following patterns: Direct Mapping. A table in the database directly corresponds to a concept in the ontology. There is a one-to-one correspondence between records in the table and instances of the concept. This pattern is modeled on the basis of the Class equivalence correspondence pattern. Join/Union. A set of database tables corresponds to a concept in the ontology when they are joined. There is a one-to-one correspondence between join records of the joined tables and instances of an ontology concept. This pattern is modeled on the Class Union pattern. Projection. A subset of the columns of a database table are needed to map a concept in the ontology. This pattern is modeled on the basis of the Class By Attribute Type pattern, where the scope of the class (the table) is restricted to the specific attributes (columns). Selection. A subset of the rows of a database table map a concept in the ontology. This pattern is modeled on the basis of the Class By Attribute Value, restricting the scope of the class (the table) to only those instances (the table rows) having a given value for the specified attributes (columns). Value transformation. A column in a table corresponds to an attribute in the ontology after transformation of its value using a transformation function. This pattern is modeled on the basis of the Attribute Transformation pattern. Combinations. Combinations of the aforementioned patterns are also possible. Another application of ontology alignment consists in maintaining consistent versions of evolving ontologies. Changes in one version of an ontology are forwarded to its previous versions via the use of correspondences. Providing specific patterns for change management helps the ontology engineer maintaining the consistence of data described using different versions of the evolving ontology. 4 Related Work Design patterns reached popularity in software engineering [9,10] as means to create a culture to document and support software design. They are reusable

8 90 F. Scharffe and D. Fensel when similar problems arise and can be extended as solutions to new problems, in that respect matching the needs of ontology alignment. Organization of software patterns was studied in [6]. In this paper, Rosengard and Ursu have proposed an ontological representation for automatic organization, retrieval, and explanation of software patterns. They suggest nine terms for the ontological representation of patterns, mainly referring to the work in [9]. Similar terms are used to model the pattern template in [3,11]. The ontological organization of patterns provides better retrieval functionalities, particularly fitting within the semantic web environment. Recent works [12,13,14] propose the use of patterns for ontology engineering. If ontologies are designed using common patterns, or best practices methods, the engineering of alignments has much to benefit from the study of ontology design patterns. Staab et al. [12] propose the use of semantic patterns to engineer ontologies while remaining independent from the underlying ontology language. The separation between patterns as modelling tools and their implementation is directly related to the Knowledge Level paradigm [15], then applied to solve recurring reasoning problems [16], and extended to general problem solving methods [17,18]. Our approach follows these works by explicitly separating correspondence patterns and grounded correspondences, as introduced Section 2. Gangemi [14] proposes the use of patterns based on experience and introduces a pattern template and a library of common patterns for ontology engineering. Blomqvist et al. [13] present a classification of the various kinds of patterns for ontology engineeringcorrespondence patterns introduced in this paper are situated at the syntactic and semantic levels of Blomqvist classification. Previous and ongoing works in ontology mediation focus on the development of tools and representation formalisms for ontology alignment. Grounded correspondences are often expressed as logical axioms, like in C-OWL [19] or Distributed Description Logics [20]. These works however do not differentiate the ontology specification from the alignment, making more difficult to maintain alignments as the ontology evolve. They also propose rules in a particular reasoning scheme that is not necessarily reusable for other tasks. The Bridge Ontology in the mapping framework MAFRA [21], the Alignment Ontology and its serializations [2,22,23] are such formalisms allowing to specify correspondences between ontologies at an abstract level. These approaches however differ from our in the sense that no explicit pattern with its description is provided. We note that apart from the MAFRA approach including a high level representation of correspondences with the Semantic Bridging Ontology, neither DDL nor C-OWL distinguish a knowledge level in the sense of [15] and thus mix the knowledge representation of alignments and the formalism used to execute these alignments in mediation tasks. There are a few mentions of patterns in the ontology matching area. In [24], Svab proposes to use patterns to obtain better automatic matching algorithms. In [25], Besana etal developped a matching algorithm exploiting communication patterns in a peer-to-peer network. Patternsas introduced in this paper were first studied in [26] and motivated the specification of the ontology alignment language in [22].

9 Correspondence Patterns for Ontology Alignment 91 5 Conclusion We have introduced ontology correspondence patterns as helpers to model ontology alignments. We ve situated patterns as an abstraction on top of the ontology alignments representation stack. Going from abstract patterns to executable mediation rules is realized by first applying a pattern in a concrete alignment task, and then at runtime grounding the correspondence to mediation rules. We set the base frame to represent correspondence patterns by using an extension of the alignment ontology. We gave a library of common patterns 14, based on the analysis of ontology mismatches. We are currently studying how to relate this work with works on ontology design patterns, the intuition being that pairs of ontology patterns could constitute the basis to construct new correspondence patterns. We are currently working on a SPARQL extension [27] able to represent ontology alignments for instance transformation. In the mid-term, we plan to investigate how patterns could be used to enhance matching algorithms, providing helpers like OMEN meta-rules [28] helping to refine existing matches. Acknowledgements The authors thank Jos de Bruijn and Jérôme Euzenat for useful discussions about correspondence patterns. References 1. Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches, pp (2005) 2. Euzenat, J., Scharffe, F., Zimmerman, A.: D2.2.10: Expressive alignment language and implementation. Project deliverable , Knowledge Web NoE (FP ) (2007) 3. Scharffe, F., Euzenat, J., Ding, Y., Fensel, D.: Correspondence patterns for ontology mediation. In: Proceedings of the Ontology Matching Workshop at ISWC, Busan, Korea. ISWC, CEUR (2007) 4. Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. In: Workshop on Ontologies and Information Sharing (2001) 5. Scharffe, F.: Omwg d7: Ontology mapping language. Technical report, Ontology Management Working Group (2007) 6. Rosengard, J.M., Ursu, M.F.: Ontological representations of software patterns. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES LNCS (LNAI), vol. 3215, pp Springer, Heidelberg (2004) 7. Ghidini, C., Serafini, L.: Reconciling concepts and relations in heterogeneous ontologies. In: ESWC, pp (2006) 8. Barrasa, J., Corcho, O., Gómez-Pérez, A.: R2o, an extensible and semantically based database-to-ontology mapping language. In: Proceedings of the 2nd Workshop on Semantic Web and Databases, Toronto, Canada (2004) 14 Available at

10 92 F. Scharffe and D. Fensel 9. Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns. Addison-Wesley Pub., Reading (1995) 10. Coplien, J.O.: Software Patterns. SIGS Books, New York (1996) 11. Scharffe, F., Euzenat, J., Fensel, D.: Towards correspondence patterns for ontology mediation. In: Proceedings of the ACM Symposium of Applied Computing, Fortaleza, Brazil. ACM, New York (2008) 12. Staab, S., Erdmann, M., Maedche, A.: Engineering ontologies using semantic patterns. In: O Leary, A.P.D. (ed.) Proceedings of the IJCAI 2001 Workshop on E- Business & the Intelligent Web, Seattle, WA, USA, August 5, 2001 (2001) 13. Blomqvist, E., Sandkuhl, K.: Patterns in ontology engineering: Classification of ontology patterns. In: ICEIS (3), pp (2005) 14. Gangemi, A.: Ontology design patterns for semantic web content. In: International Semantic Web Conference, pp (2005) 15. Newell, A.: The knowledge level. Artificial Intelligence (1982) 16. Clancey, W.J.: Heuristic classification. Artif. Intell. 27(3), (1985) 17. Wielinga, B.J., Schreiber, A.T., Breuker, J.A.: Kads: a modelling approach to knowledge engineering. Knowl. Acquis. 4(1), 5 53 (1992) 18. Fensel, D., Motta, E., Benjamins, V.R., Crubezy, M., Decker, S., Gaspari, M., Groenboom, R., Grosso, W., van Harmelen, F., Musen, M., Plaza, E., Schreiber, G., Studer, R., Wielinga, B.: The unified problem-solving method development language upml. Knowledge and Information Systems 5(1), (2002) 19. Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: Cowl: Contextualizing ontologies. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC LNCS, vol. 2870, pp Springer, Heidelberg (2003) 20. Borgida, A., Serafini, L.: Distributed description logics: Assimilating information from peer sources. Journal of Data Semantics 1, (2003) 21. Maedche, A., Motik, B., Volz, R., Silva, N.: MAFRA a mapping framework for distributed ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW LNCS (LNAI), vol. 2473, pp Springer, Heidelberg (2002) 22. Scharffe, F., de Bruijn, J.: A language to specify mappings between ontologies. In: Proc. of the Internet Based Systems IEEE Conference (SITIS 2005) (2005) 23. Euzenat, J.: An API for ontology alignment. In: Proc. 3rd international semantic web conference, Hiroshima (JP), pp (2004) 24. Svab, O.: Exploiting Patterns in Ontology Mapping. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC LNCS, vol. 4825, pp Springer, Heidelberg (2007) 25. Besana, P., Robertson, D., Rovatsos, M.: Exploiting interaction contexts in p2p ontology mapping. In: P2PKM (2005) 26. de Bruijn, J., Foxvog, D., Zimmermann, K.: Ontology mediation patterns library v1. Deliverable D4.3.1, SEKT project (IST ) (2004) 27. Polleres, A., Scharffe, F., Schindlauer, R.: Sparql++ for mapping between rdf vocabularies. In: ODBASE, On the Move Federated Conferences (2007) 28. Mitra, P., Noy, N.F., Jaiswal, A.R.: Omen: A probabilistic ontology mapping tool. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC LNCS, vol. 3729, pp Springer, Heidelberg (2005)