Constructing virtual documents for ontology matching

On the investigation of linguistic techniques used in ontology matching, we propose a new idea of virtual documents to pursue a cost-effective approach to linguistic matching in this paper. Basically, as a collection of weighted words, the virtual document of a URIref declared in an ontology contains not only the local descriptions but also the neighboring information to reflect the intended meaning of the URIref. Document similarity can be computed by traditional vector space techniques, and then be used in the similarity-based approaches to ontology matching. In particular, the RDF graph structure is exploited to define the description formulations and the neighboring operations. Experimental results show that linguistic matching based on the virtual documents is dominant in average F-Measure as compared to other three approaches. It is also demonstrated by our experiments that the virtual documents approach is cost-effective as compared to other linguistic matching approaches.

[1]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[2]  Trevor F. Cox,et al.  Metric multidimensional scaling , 2000 .

[3]  Fausto Giunchiglia,et al.  S-Match: an Algorithm and an Implementation of Semantic Matching , 2004, ESWS.

[4]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[5]  Jérôme Euzenat,et al.  OLA in the OAEI 2005 Alignment Contest , 2005, Integrating Ontologies.

[6]  Stefanos D. Kollias,et al.  A String Metric for Ontology Alignment , 2005, SEMWEB.

[7]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[8]  Yuzhong Qu,et al.  GMO: A Graph Matching for Ontologies , 2005, Integrating Ontologies.

[9]  Jeremy J. Carroll,et al.  Resource description framework (rdf) concepts and abstract syntax , 2003 .

[10]  Willem Robert van Hage,et al.  A Method to Combine Linguistic Ontology-Mapping Techniques , 2005, SEMWEB.

[11]  Georg Groh,et al.  Facilitating the Exchange of Explicit Knowledge through Ontology Mappings , 2001, FLAIRS.

[12]  Steffen Staab,et al.  QOM - Quick Ontology Mapping , 2004, GI Jahrestagung.

[13]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[14]  Silvana Castano,et al.  Global Viewing of Heterogeneous Data Sources , 2001, IEEE Trans. Knowl. Data Eng..

[15]  Yuzhong Qu,et al.  FalconAO: Aligning Ontologies with Falcon , 2005, Integrating Ontologies.

[16]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[17]  Yasuo Yamane,et al.  A Semantic Category Matching Approach to Ontology Alignment , 2004, EON.

[18]  Carolyn R. Watters,et al.  Information Retrieval and the Virtual Document , 1999, J. Am. Soc. Inf. Sci..

[19]  Vijay V. Raghavan,et al.  A critical analysis of vector space model for information retrieval , 1986, J. Am. Soc. Inf. Sci..

[20]  Mark A. Musen,et al.  The PROMPT suite: interactive tools for ontology merging and mapping , 2003, Int. J. Hum. Comput. Stud..

[21]  Max J. Egenhofer,et al.  Determining Semantic Similarity among Entity Classes from Different Ontologies , 2003, IEEE Trans. Knowl. Data Eng..

[22]  Erhard Rahm,et al.  COMA - A System for Flexible Combination of Schema Matching Approaches , 2002, VLDB.

[23]  Pedro M. Domingos,et al.  Learning to match ontologies on the Semantic Web , 2003, The VLDB Journal.

[24]  Fabien L. Gandon,et al.  On Ontology Matching Problems - for Building a Corporate Semantic Web in a Multi-Communities Organization , 2004, ICEIS.

[25]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[26]  George A. Vouros,et al.  Capturing Semantics Towards Automatic Coordination of Domain Ontologies , 2004, AIMSA.