Leveraging data and structure in ontology integration

ABSTRACT

There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems - the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25% in quality over FCA-merge and a 11% improvement in recall over COMA++.

REFERENCES

• 1: David Aumueller , Hong-Hai Do , Sabine Massmann , Erhard Rahm, Schema and ontology matching with COMA++, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066283]
• 2: Philip A. Bernstein , Sergey Melnik , John E. Churchill, Incremental schema matching, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
• 3: Peter Buneman , Susan B. Davidson , Anthony Kosky, Theoretical Aspects of Schema Merging, Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology, p.152-167, March 23-27, 1992
• 4: W. Cohen, P. Ravikumar, and S. Fienberg. A comparison of string metrics for matching names and records. In KDD Workshop on Data Cleaning and Object Consolidation, 2003.
• 5: AnHai Doan , Jayant Madhavan , Pedro Domingos , Alon Halevy, Learning to map between ontologies on the semantic web, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511532]
• 6: A. Doan, J. Madhavan, P. Domingos, and A. Y. Halevy. Ontology Matching: A Machine Learning Approach. In Handbook on Ontologies, pages 385--404. Springer-Verlag, 2004.
• 7: M. Ehrig and S. Staab. QOM-Quick Ontology Mapping. In ISWC, pages 683--697, 2004.
• 8: J. Euzenat, D. Loup, M. Touzani, and P. Valtchev. Ontology alignment with OLA. In ISWC EON, pages 59--68, 2004.
• 9: F. Giunchiglia, P. Shvaiko, and M. Yatskevich. S-Match: an algorithm and an implementation of semantic matching. In Semantic Interoperability and Integration, number 04391 in Dagstuhl Sem. Proc., 2005.
• 10: I. Horrocks, P. Patel-Schneider, and F. van Harmelen. From SHIQ and RDF to OWL: The making of a web ontology language. Journal of Web Semantics, 1(1):7--26, 2003.
• 11: I. Horrocks, U. Sattler, and S. Tobies. Practical reasoning for very expressive description logics. Logic J. of the IGPL, 8(3):239--264, 2000.
• 12: Y. Kalfoglou and M. Schorlemmer. If-map: an ontology mapping method based on information flow theory. J. Data Sem., 1(1):98--127, Oct. 2003.
• 13: Maurizio Lenzerini, Data integration: a theoretical perspective, Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 03-05, 2002, Madison, Wisconsin [doi>10.1145/543613.543644]
• 14: Wen-Syan Li , Chris Clifton, SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks, Data & Knowledge Engineering, v.33 n.1, p.49-84, April 2000 [doi>10.1016/S0169-023X(99)00044-0]
• 15: J. Madhavan, P. A. Bernstein, A. Doan, and A. Halevy. Corpus-based schema matching. ICDE, 0:57--68, 2005.
• 16: D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. An environment for merging and testing large ontologies. In KR, pages 483--493, 2000.
• 17: S. Melnik, H. Garcia-Molina, and E. Rahm. Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching. ICDE, page 117, 2002.
• 18: Renée J. Miller , Laura M. Haas , Mauricio A. Hernández, Schema Mapping as Query Discovery, Proceedings of the 26th International Conference on Very Large Data Bases, p.77-88, September 10-14, 2000
• 19: F. Naumann, A. Bilke, J. Bleiholder, and M. Weis. Data fusion in three steps: Resolving schema, tuple, and value inconsistencies. IEEE Data Eng. Bull., 29(2):21--31, 2006.
• 20: Natalya F. Noy , Mark A. Musen, The PROMPT suite: interactive tools for ontology merging and mapping, International Journal of Human-Computer Studies, v.59 n.6, p.983-1024, December 2003 [doi>10.1016/j.ijhcs.2003.08.002]
• 21: C. Patel, K. Supekar, and Y. Lee. OntoGenie: Extracting Ontology Instances from WWW. In Human Language Technology for the Semantic Web and Web Services, ISWC, 2003.
• 22: L. Popa, Y. Velegrakis, R. J. Miller, M. A. Hernàndez, and R. Fagin. Translating Web Data. In VLDB, pages 598--609, 2002.
• 23: R. Pottinger and P. A. Bernstein. Merging Models Based on Given Correspondences. In VLDB, pages 826--873, 2003.
• 24: 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]
• 25: P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In IJCAI, pages 448--453, 1995.
• 26: N. Silva and J. Rocha. Ontology mapping for interoperability in semantic web. In ICWI, pages 603--610, 2003.
• 27: E. Sirin and B. Parsia. Pellet: An OWL DL Reasoner. In Description Logics, volume 104 of CEUR Work. Proc., 2004.
• 28: G. Stumme and A. Maedche. FCA-MERGE: Bottom-Up Merging of Ontologies. In IJCAI, pages 225--234, 2001.
• 29: W. Winkler. Advanced methods for record linkage., 1994. Technical report, Statistical Research Division, Washington, DC: U.S. Bureau of the Census.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Semantic networks

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
        D.2.12 Interoperability
            Subjects: Data mapping

General Terms:
Algorithms, Experimentation

Keywords:
data integration, logical inference, ontology alignment, schema mapping, statistical inference