Acquiring advanced properties in ontology mapping

ABSTRACT

With the emergence of the Semantic Web, ontologies are being exposed on the Web to support a variety of applications, including enhanced search and discovery, rapid enterprise integration and cross-domain knowledge sharing. This explosion of knowledge sharing on the web is also leading to the formation of knowledge silos and a growing need for integration of these sources. Automated solutions to mapping ontologies are emerging that address this growing need with very promising results. However, most approaches have focused on mapping ontologies using relationships of similarity and equivalence and very few have applied knowledge in upper ontologies. We built algorithms to acquire relationships between ontological components beyond similarity and equivalence that include hyponymy and more importantly, algorithms to map ontologies based on relationships that are specified within the ontologies. These algorithms employ the semantics of OWL in conjunction with online linguistic resources and upper ontologies. Initial test results are promising.

REFERENCES

• 1: Berners-Lee, T., Hendler, J., and Lasilla, O.: The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May 2001.
• 2: McGuinness, D. L., van Harmelen, F.: Web Ontology Language (OWL), W3C Recommendation, 10 February, 2004.
• 3: Jérôme Euzenat , Pavel Shvaiko, Ontology Matching, Springer-Verlag New York, Inc., Secaucus, NJ, 2007
• 4: Erhard Rahm , Philip A. Bernstein, A survey of approaches to automatic schema matching, The VLDB Journal — The International Journal on Very Large Data Bases, v.10 n.4, p.334-350, December 2001 [doi>10.1007/s007780100057]
• 5: Natalya Fridman Noy , Mark A. Musen, PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment, Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, p.450-455, July 30-August 03, 2000
• 6: AnHai Doan , Alon Y. Halevy, Semantic-integration research in the database community, AI Magazine, v.26 n.1, p.83-94, March 2005
• 7: Natalya F. Noy, Semantic integration: a survey of ontology-based approaches, ACM SIGMOD Record, v.33 n.4, December 2004 [doi>10.1145/1041410.1041421]
• 8: Palopoli, L., Sacca, D., Ursino, D.: DIKE: a system supporting the semi-automatic construction of cooperative information systems from heterogeneous databases, in Proc. 7th International Conference on Information and Knowledge Management (CIKM), pp. 306--313, Bethesda, Maryland, 1998.
• 9: Kotis, K., Vouros, G. Stergiou, K.: Towards automatic merging of domain ontologies: the HCONE-merge approach, in Journal of Web Semantics, 4(1): pp. 60--79, 2006.
• 10: Jaehong Kim , Minsu Jang , Young-Guk Ha , Joo-Chan Sohn , Sang Jo Lee, MoA: OWL ontology merging and alignment tool for the semantic web, Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence, p.722-731, June 22-24, 2005, Bari, Italy [doi>10.1007/11504894_100]
• 11: Zimmerman, A., Krotzsch, M. Euzenat, J., Hitzler, P.: Formalizing ontology alignment and its operations with category theory, in Proc. of 4th International Conference on Formal Ontology in Information Systems (FOIS), pp. 277--288, Baltimore, Maryland, 2006.
• 12: Euzénat, J., Mochol, M., Shvaiko, P., Stuckenschmidt, H., Svab, O., Svatek, V., van Hage, W. R., Yatskevich, M.: Results of the Ontology Alignment Initiative 2006, in Proc. International Workshop on Ontology Matching, co-located with 5th International Semantic Web Conference, Athens, Georgia, 2006.
• 13: Sahoo, S., Zeng, K., Bodenreider, O., Sheth, A.: From 'Glycosyltransferase' to 'Congenital Muscular Dystrophy': Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology," in Student Health Technology Inform. 129 pp. 1260--1264, 2007.
• 14: Songmao Zhang , Peter Mork , Olivier Bodenreider , Philip A. Bernstein, Comparing two approaches for aligning representations of anatomy, Arificial Intelligence in Medicine, v.39 n.3, p.227-236, March, 2007 [doi>10.1016/j.artmed.2006.12.002]
• 15: Elmasri, R., Fu, J., Ji, F., Li, Q.: BioSO: Bioinformatic Service Ontology for Dynamic Biomedical Web Services Integration, Proceedings of the Fourth Biotechnology and Bioinformatics Symposium (BIOT 2007), pp. 60--62, Kalita and Mattoon, Eds., Colorado Springs, CO, October 2007.
• 16: Tiffin, N., Kelso, J. F., Powell, A. R., Pan, H., Bajic, V. B., Hide, W. A.: Integration of text- and data-mining using ontologies successfully selects disease gene candidates, Nucleic Acids Research, 33(5): pp.1544--1552, 2005.
• 17: Marc Ehrig, Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 18: Euzenat, J. 2007. Semantic Precision and Recall for Ontology Alignment Evaluation. IJCAI 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

General Terms:
Algorithms, Experimentation, Languages, Theory

Keywords:
knowledge acquisition, knowledge management, ontology, ontology mapping, semantic technology