Octavian Udrea
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++.
- D. Aumueller, H. H. Do, S. Massmann, and E. Rahm. Schema and ontology matching with COMA++. In Proc. SIGMOD, pages 906--908, 2005. Google Scholar
Digital Library
- P. A. Bernstein, S. Melnik, and J. E. Churchill.Incremental schema matching. In Proc. VLDB, pages 1167--1170, 2006. Google ScholarDigital Library
- P. Buneman, S. Davidson, and A. Kosky. Theoretical Aspects of Schema Merging. In EDBT, pages 152--167, 1992. Google ScholarDigital Library
- 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.Google Scholar
- A. Doan, J. Madhavan, P. Domingos, and A. Y. Halevy. Learning to map between ontologies on the semantic web. In WWW, pages 662--673, 2002. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- M. Ehrig and S. Staab. QOM-Quick Ontology Mapping. In ISWC, pages 683--697, 2004.Google ScholarDigital Library
- J. Euzenat, D. Loup, M. Touzani, and P. Valtchev. Ontology alignment with OLA. In ISWC EON, pages 59--68, 2004.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- I. Horrocks, U. Sattler, and S. Tobies. Practical reasoning for very expressive description logics. Logic J. of the IGPL, 8(3):239--264, 2000.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- M. Lenzerini. Data Integration: A Theoretical Perspective. In PODS, pages 233--246, 2002. Google ScholarDigital Library
- W. S. Li and C. Clifton. Semint: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data Knowledge Eng., 33(1):49--84, 2000. Google ScholarDigital Library
- J. Madhavan, P. A. Bernstein, A. Doan, and A. Halevy. Corpus-based schema matching. ICDE, 0:57--68, 2005. Google ScholarDigital Library
- D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. An environment for merging and testing large ontologies. In KR, pages 483--493, 2000.Google ScholarDigital Library
- 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. Google ScholarDigital Library
- R. J. Miller, L. M. Haas, and M. Hernàndez. Schema Mapping as Query Discovery. In VLDB, pages 77--88, 2000. Google ScholarDigital Library
- 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.Google Scholar
- N. F. Noy and M. A. Musen. The PROMPT suite: interactive tools for ontology merging and mapping. Int. J. of Hum. Comp. Stud., 59(6):983--1024, 2003. Google ScholarDigital Library
- 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.Google Scholar
- L. Popa, Y. Velegrakis, R. J. Miller, M. A. Hernàndez, and R. Fagin. Translating Web Data. In VLDB, pages 598--609, 2002. Google ScholarDigital Library
- R. Pottinger and P. A. Bernstein. Merging Models Based on Given Correspondences. In VLDB, pages 826--873, 2003. Google ScholarDigital Library
- E. Rahm and P. A. Bernstein. A Survey of Approaches to Automatic Schema Matching. The VLDB Journal, 10(4):334--350, 2001. Google ScholarDigital Library
- P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In IJCAI, pages 448--453, 1995. Google ScholarDigital Library
- N. Silva and J. Rocha. Ontology mapping for interoperability in semantic web. In ICWI, pages 603--610, 2003.Google Scholar
- E. Sirin and B. Parsia. Pellet: An OWL DL Reasoner. In Description Logics, volume 104 of CEUR Work. Proc., 2004.Google Scholar
- G. Stumme and A. Maedche. FCA-MERGE: Bottom-Up Merging of Ontologies. In IJCAI, pages 225--234, 2001. Google ScholarDigital Library
- W. Winkler. Advanced methods for record linkage., 1994. Technical report, Statistical Research Division, Washington, DC: U.S. Bureau of the Census.Google Scholar
Index Terms
- Leveraging data and structure in ontology integration
Recommendations
Ontology alignment design patterns
Interoperability between heterogeneous ontological descriptions can be performed through ontology mediation techniques. At the heart of ontology mediation lies the alignment: a specification of correspondences between ontology entities. Ontology ...
Ontology alignment for semantic data integration through foundational ontologies
ER'12: Proceedings of the 2012 international conference on Advances in Conceptual ModelingOntology alignment is the process of finding corresponding entities with the same intended meaning in different ontologies. In scenarios where an ontology conceptually describes the contents of a data repository, this provides valuable information for ...
An Ontology for Clinical Trial Data Integration
SMC '13: Proceedings of the 2013 IEEE International Conference on Systems, Man, and CyberneticsA set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical ...
Comments