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Ontology Translation by Ontology Merging and Automated Reasoning

  • Dejing Dou
  • Drew McDermott
  • Peishen Qi
Conference paper
Part of the Whitestein Series in Software Agent Technologies book series (WSSAT)

Abstract.

Ontology translation is one of the most difficult problems that web-based agents must cope with. An ontology is a formal specification of a vocabulary, including axioms relating its terms. Ontology translation is best thought of in terms of ontology merging. The merge of two related ontologies is obtained by taking the union of the terms and the axioms defining them. We add bridging axioms not only as “bridges” between terms in two related ontologies but also to make this merge into a complete new ontology for further merging with other ontologies. Translation is implemented using an inference engine (OntoEngine), running in either a demand-driven (backward-chaining) or data-driven (forward chaining) mode. We illustrate our method by describing its application in an online ontology translation system, OntoMerge, which translates a dataset in the DAML notation to a new DAML dataset that captures the same information, but in a different ontology. A uniform internal representation, Web-PDDL is used for representing merged ontologies and datasets for automated reasoning.

Keywords

Description Logic Inference Engine Automate Reasoning Horn Clause Related Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    http://www.w3c.org/TR./wsdlGoogle Scholar
  2. [2]
    http://www.daml.orgGoogle Scholar
  3. [3]
    http://www.w3.org/TR./owl-guideGoogle Scholar
  4. [4]
    http://www.cs.yale.edu/homes/dvm/daml/ontologies/daml/yale_bib.damlGoogle Scholar
  5. [5]
    http://www.daml.ri.cmu.edu/ont/homework/atlas-publications.damlGoogle Scholar
  6. [6]
    http://cs-www.cs.yale.edu/homes/ddj/ontologies/yale_bib_ont.pddlGoogle Scholar
  7. [7]
    http://cs-www.cs.yale.edu/homes/ddj/ontologies/cmu_atlas_publications.pddlGoogle Scholar
  8. [8]
    http://cs-www.cs.yale.edu/homes/dvm/daml/ontologies/pddl/cmu_yale_bib_merge.pddlGoogle Scholar
  9. [9]
    http://jena.sourceforge.net/tutorial/RDF_API/index.htmlGoogle Scholar
  10. [10]
    http://www.ksl.stanford.edu/software/JTPGoogle Scholar
  11. [11]
    http://cs-www.cs.yale.edu/homes/dvm/daml/ontology-translation.htmlGoogle Scholar
  12. [12]
    http://cs-www.cs.yale.edu/homes/dvm/daml/pddl_daml_translator.htmlGoogle Scholar
  13. [13]
    http://www.daml.org/2002/04/geonames/geonames-ont.damlGoogle Scholar
  14. [14]
    http://www.daml.org/2001/10/html/airport-ont.damlGoogle Scholar
  15. [15]
    http://www.daml.org/2001/06/map/map-ont.damlGoogle Scholar
  16. [16]
    http://www.daml.org/2001/01/gedcom/gedcom.damlGoogle Scholar
  17. [17]
    http://orlando.drc.com/daml/Ontology/Genealogy/3.1/Gentology-ont.damlGoogle Scholar
  18. [18]
    S. Adali, K. Candan, Y. Papakonstantinou, and V. S. Subrahmanian. Query caching and optimization in distributed mediator systems. In Proc. ACM SIGMOD Conf. on Management of Data, pages 137–148, 1996.Google Scholar
  19. [19]
    F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. Patel-Schneider. The Description Logic Handbook; Theory, Implementation, and Applications. Cambridge University Press, 2003.Google Scholar
  20. [20]
    F. Baader and W. Nutt. Basic description logics. In F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. Patel-Schneider, editors, The Description Logic Handbook; Theory, Implementation, and Applications, pages 43–95. Cambridge University Press, 2003.Google Scholar
  21. [21]
    D. Beneventano, S. Bergamaschi, F. Guerra, and M. Vincini. The MOMIS approach to information integration. In ICEIS (1), pages 194–198, 2001.Google Scholar
  22. [22]
    S. Buvac and R. Fikes. A declarative formalization of knowledge translation. In Proceedings of the ACM CIKM: The 4th International Conference on Information and Knowledge Management, 1995.Google Scholar
  23. [23]
    H. Chalupsky. Ontomorph: A translation system for symbolic logic. In Proc. Int’l. Con. on Principles of Knowledge Representation and Reasoning, pages 471–482, 2000. San Francisco: Morgan Kaufmann.Google Scholar
  24. [24]
    A. Doan, J. Madhavan, P. Domingos, and A. Halevy. Learning to map between ontologies on the semantic web. In Proceedings of the World-Wide Web Conference (WWW-2002), 2002.Google Scholar
  25. [25]
    D. Dou, D. McDermott, and P. Qi. Ontology translation on the semantic web. In Proc. Int’l Conf. on Ontologies, Databases and Applications of SEmantics (ODBASE) 2003, pages 952–969, 2003. LNCS 2888 Springer-Verlag.Google Scholar
  26. [26]
    C. Fellbaum, editor. WordNet: An Electronic Lexical Database. MIT Press, 1998.Google Scholar
  27. [27]
    Khun Lee Fung XSLT: Working with XML and HTML. Addison-Wesley 2001Google Scholar
  28. [28]
    M. R. Genesereth, A. M. Keller, and O. M. Duschka. Infomaster: an information integration system. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 539–542, 1997.Google Scholar
  29. [29]
    T. Gruber. Ontolingua: A Translation Approach to Providing Portable Ontology Specifications. Knowledge Acquisition, 5(2):199–200, 1993.CrossRefGoogle Scholar
  30. [30]
    P. Haase, J. Broekstra, A. Eberhart, and R. Volz. A comparison of rdf query languages. In Proceedings of the Third International Semantic Web Conference, Hiroshima, Japan, 2004., NOV 2004.Google Scholar
  31. [31]
    A. Y. Halevy. Answering queries using views: A survey. VLDB J, 10(4):270–294, 2001.CrossRefGoogle Scholar
  32. [32]
    I. Horrocks and P. F. Patel-Schneider. FaCT and DLP. Lecture Notes in Computer Science, 1397:27, 1998.Google Scholar
  33. [33]
    J. Madhavan, P. A. Bernstein, P. Domingos, and A. Halevy. Representing and Reasoning about Mappings between Domain Models. In Proc. AAAI 2002, 2002.Google Scholar
  34. [34]
    J. Madhavan, P. A. Bernstein, and E. Rahm. Generic schema matching with cupid. In Proc. 27th Int. Conf. on Very Large Data Bases (VLDB).Google Scholar
  35. [35]
    A. Maedche, B. Motik, N. Silva, and R. Volz., mafra — a mapping framework for distributed ontologies. In Proceedings of EKAW2002, pages 235–250, 2002.Google Scholar
  36. [36]
    D. McDermott. The Planning Domain Definition Language Manual. Technical Report 1165, Yale Computer Science, 1998. (CVC Report 98-003) Available at ftp://ftp.cs.yale.edu/pub/mcdermott/software/pddl.tar.gz.Google Scholar
  37. [37]
    D. McDermott, M. Burstein, and D. Smith. Overcoming ontology mismatches in transactions with self-describing service agents. In Proc. Semantic Web Working Symposium, pages 285–302, 2001.Google Scholar
  38. [38]
    D. McDermott and D. Dou. Representing Disjunction and Quantifiers in RDF. In Proceedings of International semantic Web Conference 2002, pages 250–263, 2002.Google Scholar
  39. [39]
    D. McGuinness, R. Fikes, J. Rice, and S. Wilder. An Environment for Merging and Testing Large Ontologies. In Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000), 2000.Google Scholar
  40. [40]
    N. F. Noy and M. A. Musen. Prompt: Algorithm and tool for automated ontology merging and alignment. Technical Report 2000-0831, Proc. AAAI 17 Also available as Stanford SMI, 2000.Google Scholar
  41. [41]
    S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (2nd edition). Prentice Hall, 2002.Google Scholar
  42. [42]
    L. Serafini, P. Bouquet, B. Magnini, and S. Zanobini. An algorithm for matching contextualized schemas via sat. In Proc. CONTEX’03S, 2003.Google Scholar
  43. [43]
    G. Stumme and A. Maedche. Ontology merging for federated ontologies on the semantic web. In Proceedings of the International Workshop for Foundations of Models for Information Integration (FMII-2001), September 2001.Google Scholar
  44. [44]
    Jeffrey D. Ullman Principles of Database and Knowledge-Base Systems, 1. New York: Computer Science Press. 1988aGoogle Scholar
  45. [45]
    Jeffrey D. Ullman Principles of Database and Knowledge-Base Systems. 2 New York: Computer Science Press 1988bGoogle Scholar

Copyright information

© Birkhäuser Verlag 2005

Authors and Affiliations

  • Dejing Dou
    • 1
  • Drew McDermott
    • 2
  • Peishen Qi
    • 2
  1. 1.Department of Computer and Information Science120 Deschutes Hall University of OregonEugene
  2. 2.Computer Science DepartmentYale UniversityNew Haven

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