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Semantic Conflict Resolution Ontology (SCROL): An Ontology for Detecting and Resolving Data and Schema-Level Semantic Conflicts
February 2004 (vol. 16 no. 2)
pp. 189-202
Sudha Ram, IEEE

Abstract—Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called Semantic Conflict Resolution Ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.

[1] C. Batini and M. Lenzerini, A Methodology for Data Schema Integration in the Entity Relationship Model IEEE Trans. Software Eng., vol. 10, no. 8, pp. 650-664, Nov. 1984.
[2] Y.-H. Chang and L. Raschid, Producing Interoperable Queries for Relational and Object-Oriented Databases J. Intelligent Information Systems, vol. 14, no. 1, pp. 51-75, Mar. 2000.
[3] C. Collet, M.N. Huhns, and W.-M. Shen, "Resource Integration Using a Large Knowledge Base in Carnot," Computer, Vol. 24, No. 12, Dec. 1991, pp.55-62.
[4] S. Dao and B. Perry, Applying a Data Miner to Heterogeneous Schema Integration Proc. First Int' l Conf. Knowledge Discovery and Data Mining, pp. 63-68, Aug. 1995.
[5] C.H. Goh, S. Bressan, S.E. Madnick, and M.D. Siegel, Context Interchange: New Features and Formalisms for the Intelligent Integration of Information ACM Trans. Information Systems, vol. 17, no. 3, pp. 270-293, July 1999.
[6] C.H. Goh, S.E. Madnick, and M.D. Siegel, Context Interchange: Overcoming the Challenges of Large-Scale Interoperable Database Systems in a Dynamic Environment Proc. Third Int'l Conf. Information and Knowledge Management, pp. 337-346, 1994.
[7] T.R. Gruber, The Role of Common Ontology in Achieving Sharable, Reusable Knowledge Bases Proc. Second Int'l Conf. Principles of Knowledge Representation and Reasoning, pp. 601-602, 1991.
[8] T.R. Gruber, Toward Principles for the Design of Ontologies Used for Knowledge Sharing Technical Report KSL 93-04, Knowledge Systems Laboratory, Stanford Univ., 1993.
[9] T.R. Gruber, A Translation Approach to Portable Ontology Specifications Knowledge Acquisition, vol. 5, no. 2, pp. 199-220, June 1993.
[10] M.N. Huhns and M.P. Singh, Managing Heterogeneous Transaction Workflows with Co-Operating Agents Agent Technology: Foundations, Applications, and Markets, N.R. Jennings and M.J. Wooldridge, eds., pp. 219-239, Berlin: Springer, 1998.
[11] J. Kahng and D. McLeod, Dynamic Classificational Ontologies: Mediation of Information Sharing in Cooperative Federated Database Systems Cooperative Information Systems: Trends and Directions, M.P. Papazoglou and G. Sohlageter, eds., pp. 179-203, San Diego, Calif.: Academic Press, 1998.
[12] V. Kashyap and A.P. Sheth, Semantic and Schematic Similarities Between Objects in Databases: A Context-Based Approach Technical Report TR-CS-95-001, Dept. of Computer Science, Univ. of Georgia, 1995.
[13] V. Kashyap and A.P. Sheth, Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach The VLDB J., vol. 5, no. 4, pp. 276-304, Dec. 1996.
[14] V. Kashyap and A.P. Sheth, Semantic Heterogeneity in Global Information Systems: The Role of Metadata, Context and Ontologies Cooperative Information Systems: Trends and Directions, M.P. Papazoglou and G. Schlageter, eds., pp. 139-178, San Diego, Calif.: Academic Press, 1998.
[15] M. Kifer and G. Lausen, F-Logic: A Higher-Order Language for Reasoning about Objects, Inheritance, and Scheme Proc. ACM SIGMOD Int'l Conf., pp. 134-146, 1989.
[16] D.B. Lenat, R.V. Guha, K. Pittman, D. Pratt, and M. Shepherd, CYC: Toward Programs with Common Sense Comm. ACM, vol. 33, no. 8, pp. 30-49, Aug. 1990.
[17] R. MacGregor, The Evolving Technology of Classification-Based Knowledge Representation Systems Principles of Semantic Networks: Explorations in the Representation of Knowledge, J.F. Sowa, ed., pp. 385-400, San Mateo, Calif.: Morgan Kaufmann, 1991.
[18] J. Madhavan, P.A. Bernstein, and E. Rahm, Generic Schema Matching with Cupid Proc. VLDB Conf., pp. 49-58, Sept. 2001.
[19] K. Mahalingam and M.N. Huhns, An Ontology Tool for Query Formulation in an Agent-Based Context Proc. Second IFCIS Int'l Conf. Cooperative Information Systems, June 1997.
[20] K. Mahesh and S. Nirenburg, A Situated Ontology for Practical NLP Proc. IJCAI-95 Workshop Basic Ontological Issues in Knowledge Sharing, Aug. 1995.
[21] R.J. Miller, M.A. Hernandez, L.M. Hass, L. Yan, C.T.H. Ho, R. Fagin, and L. Popa, The Clio Project: Managing Heterogeneity SIGMOD Record, vol. 30, no. 1, pp. 78-83, Mar. 2001.
[22] T.L. Nyerges, Schema Integration Analysis for the Development of GIS Databases Int'l J. Geographical Information System, vol. 3, no. 2, pp. 153-183, 1989.
[23] A.M. Ouksel, A Framework for a Scalable Agent Architecture of Cooperating Heterogeneous Knowledge Sources Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet, M. Klusch, ed., pp. 100-124, Berlin: Springer, 1999.
[24] A.M. Ouksel and C.F. Naiman, Coordinating Context Building in Heterogeneous Information Systems J. Intelligent Information Systems, vol. 3, no. 2, pp. 151-183, Apr. 1994.
[25] J. Park, Facilitating Interoperability among Heterogeneous Geographic Database Systems: A Theoretical Framework, A Prototype System, and Evaluation, Dissertation PhD thesis, Dept. of Management Information Systems, Univ. of Arizona, 1999.
[26] J. Park and S. Ram, A Conflict Resolution Environment for Autonomous Mediation Among Heterogeneous Databases Technical Report 01-05, Univ. of Minnesota, Apr. 2001.
[27] R.S. Patil, R.E. Fikes, P.F. Patel-Schneider, D. McKay, T. Finin, T. Gruber, and R. Neches, The DARPA Knowledge Sharing Effort: Progress Report Proc. Third Int'l Conf. Principles of Knowledge Representation and Reasoning, pp. 777-788, Oct. 1992.
[28] J.-C.R. Pazzaglia and S.M. Embury, Bottom-Up Integration of Ontologies in a Database Context Proc. Fifth KRDB Workshop, May 1998.
[29] S. Ram, J. Park, and G. Ball, Semantic Model Support for Geographic Information Systems Computer, vol. 32, no. 5, pp. 74-81, May 1999.
[30] S. Ram, J. Park, K. Kim, and Y. Hwang, A Comprehensive Framework for Classifying Data- and Schema-Level Semantic Conflicts in Geographic and Non-Geographic Databases Proc. Ninth Workshop Information Technologies and Systems, pp. 185-190, Dec. 1999.
[31] S. Ram, J. Park, and D. Lee, Digital Libraries for the Next Millennium: Challenges and Research Directions Information Systems Frontiers, vol. 1, no. 1, pp. 75-94, July 1999.
[32] S. Ram and V. Ramesh, Schema Integration: Past, Current and Future Management of Heterogeneous and Autonomous Database Systems, A. Elmagarmid, M. Rusinkeiwicz, and A.P. Sheth, eds., pp. 119-155, San Francisco: Morgan Kaufmann, 1999.
[33] V. Ramesh, K. Canfield, S. Quirologico, and M. Silva, An Intelligent Agent-Based Architecture for Interoperability among Heterogeneous Medical Databases Proc. Second Am. Conf. Information Systems, pp. 549-551, Aug. 1996.
[34] A.P. Sheth, Semantic Issues in Multidatabase Systems SIGMOD Record, vol. 40, no. 4, pp. 5-9, Dec. 1991.
[35] A.P. Sheth and J.A. Larson, Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases ACM Computing Surveys, vol. 22, no. 3, pp. 184-236, Sept. 1990.
[36] M. Siegel, S. Madnick, and E. Sciore, Context Interchange in a Client-Server Architecture J. Systems Software, vol. 27, no. 3, pp. 223-232, Dec. 1994.
[37] V.C. Storey, R.H.L. Chiang, D. Dey, R.C. Goldstein, and S. Sundaresan, Database Design with Common Sense Business Reasoning and Learning ACM Trans. Database Systems, vol. 22, no. 4, pp. 471-512, Dec. 1997.
[38] H. Takeda, K. Iwata, M. Takaai, A. Sawada, and T. Nishida, An Ontology-Based Cooperative Environment for Real World Agents Proc. Int'l Conf. Multiagent Systems, pp. 353-360, Dec. 1996.
[39] P.E. Vet and N.J. Mars, "Bottom-Up Construction of Ontologies," IEEE Trans. Knowledge and Data Eng., vol. 10, no. 4, July/Aug. 1998, pp. 513-526.

Index Terms:
Heterogeneous databases, ontology, semantic conflict resolution, semantic modeling.
Sudha Ram, Jinsoo Park, "Semantic Conflict Resolution Ontology (SCROL): An Ontology for Detecting and Resolving Data and Schema-Level Semantic Conflicts," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 2, pp. 189-202, Feb. 2004, doi:10.1109/TKDE.2004.1269597
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