A gauss function based approach for unbalanced ontology matching

ABSTRACT

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight ontology which represents the local domain knowledge, we extract a "similar" sub-ontology from the corresponding heavyweight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly calculate the influence values between concepts. In addition, we perform an extensive experiment to verify the effectiveness and efficiency of our proposed approach by using OAEI2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time.

REFERENCES

• 1: GEMET homepage http://www.eionet.europa.eu/gemet.
• 2: Ontology Alignment Evaluation Initiativehttp://oaei.ontologymatching.org/.
• 3: GEMET download site http://oaei.ontologymatching.org/2007/environment/gemet/gemet_2007_OWL.zip.
• 4: AGROVOC homepage http://www.fao.org/aims/ag_intro.htm.
• 5: AGROVOC download site http://oaei.ontologymatching.org/2007/food/agrovoc/agrovoc_2007_OWL.zip.
• 6: NAL homepage http://agclass.nal.usda.gov/agt/.
• 7: NAL download site http://oaei.ontologymatching.org/2007/food/nalt_2007_OWL.zip.
• 8: Golden Standard download site http://oaei.ontologymatching.org/2007/results/environment/gold standard/.
• 9: Zharko Aleksovski, Michel Klein, Warner ten Kate, and Frank van Harmelen. Matching Unstructured Vocabularies Using a Background Ontology. In Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management (EKAW), 2006.
• 10: Yuan An, Alex Borgida, and John Mylopoulos. Discovering the Semantics of Relational Tables Through Mappings. Journal on Data Semantics, 7:1--32,2006.
• 11: Alexander Budanitsky , Graeme Hirst, Evaluating WordNet-based Measures of Lexical Semantic Relatedness, Computational Linguistics, v.32 n.1, p.13-47, March 2006 [doi>10.1162/coli.2006.32.1.13]
• 12: Silvana Castano, Alfio Ferrara, and Stefano Montanelli. Matching Ontologies in Open Networked Systems: Techniques and Applications. Journal on Data Semantics,V:25--63, 2006.
• 13: Laura Chiticariu , Phokion G. Kolaitis , Lucian Popa, Interactive generation of integrated schemas, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 09-12, 2008, Vancouver, Canada [doi>10.1145/1376616.1376700]
• 14: William W. Cohen, Pradeep Ravikumar, and Stephen E. Fienberg. A Comparison of String Metrics for Matching Names and Records. In Proceedings of 9th International Conference on Knowledge Discovery and Data Mining (KDD) Workshop on Data Cleaning and Object Consolidation, 2003.
• 15: Hong-Hai Do , Erhard Rahm, COMA: a system for flexible combination of schema matching approaches, Proceedings of the 28th international conference on Very Large Data Bases, p.610-621, August 20-23, 2002, Hong Kong, China
• 16: AnHai Doan , Pedro Domingos , Alon Y. Halevy, Reconciling schemas of disparate data sources: a machine-learning approach, Proceedings of the 2001 ACM SIGMOD international conference on Management of data, p.509-520, May 21-24, 2001, Santa Barbara, California, United States
• 17: AnHai Doan , Alon Y. Halevy, Semantic-integration research in the database community, AI Magazine, v.26 n.1, p.83-94, March 2005
• 18: Risto Gligorov , Warner ten Kate , Zharko Aleksovski , Frank van Harmelen, Using Google distance to weight approximate ontology matches, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242676]
• 19: Laura M. Haas , Mauricio A. Hernández , Howard Ho , Lucian Popa , Mary Roth, Clio grows up: from research prototype to industrial tool, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066252]
• 20: Hai He , Weiyi Meng , Clement Yu , Zonghuan Wu, WISE-Integrator: a system for extracting and integrating complex web search interfaces of the deep web, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway
• 21: Antoine Isaac, Lourens van der Meij, Stefan Schlobach, and Shenghui Wang. An Empirical Study of Instance-Based Ontology Matching. In Proceedings of The 6th International Semantic Web Conference and the 2nd Asian Semantic Web Conference (ISWC/ASWC), 2007.
• 22: Mong Li Lee , Liang Huai Yang , Wynne Hsu , Xia Yang, XClust: clustering XML schemas for effective integration, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA [doi>10.1145/584792.584841]
• 23: Douglas B. Lenat , R. V. Guha, Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 24: Dekang Lin, An Information-Theoretic Definition of Similarity, Proceedings of the Fifteenth International Conference on Machine Learning, p.296-304, July 24-27, 1998
• 25: Jayant Madhavan , Philip A. Bernstein , Erhard Rahm, Generic Schema Matching with Cupid, Proceedings of the 27th International Conference on Very Large Data Bases, p.49-58, September 11-14, 2001
• 26: Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching, Proceedings of the 18th International Conference on Data Engineering, p.117, February 26-March 01, 2002
• 27: Tova Milo , Sagit Zohar, Using Schema Matching to Simplify Heterogeneous Data Translation, Proceedings of the 24rd International Conference on Very Large Data Bases, p.122-133, August 24-27, 1998
• 28: 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]
• 29: Jie Tang , Juanzi Li , Bangyong Liang , Xiaotong Huang , Yi Li , Kehong Wang, Using Bayesian decision for ontology mapping, Web Semantics: Science, Services and Agents on the World Wide Web, v.4 n.4, p.243-262, December, 2006 [doi>10.1016/j.websem.2006.06.001]
• 30: Octavian Udrea , Lise Getoor , Renée J. Miller, Leveraging data and structure in ontology integration, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247531]
• 31: Shenghui Wang , Gwenn Englebienne , Stefan Schlobach, Learning Concept Mappings from Instance Similarity, Proceedings of the 7th International Conference on The Semantic Web, October 26-30, 2008, Karlsruhe, Germany [doi>10.1007/978-3-540-88564-1_22]

INDEX TERMS

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

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

General Terms:
Algorithms, Experimentation, Performance

Keywords:
gauss function, ontology matching, unbalance