ABSTRACT
Many AI tasks require determining whether two knowledge representations encode the same knowledge. Solving this matching problem is hard because representations may encode the same content but differ substantially in form. Previous approaches to this problem have used either syntactic measures, such as graph edit distance, or semantic knowledge to determine the "distance" between two representations. Although semantic approaches outperform syntactic ones, previous research has focused primarily on the use of taxonomic knowledge. We show that this is not enough because mismatches between representations go largely unaddressed. In this paper, we describe how transformations can augment existing semantic approaches to further improve matching. We also describe the application of our approach to the task of critiquing military Courses of Action and compare its performance to other leading algorithms.
- K. Barker, J. Blythe, G. Borchardt, V. Chaudhri, P. Clark, P. Cohen, J. Fitzgerald, K. Forbus, Y. Gil, B. Katz, J. Kim, G. King, S. Mishra, C. Morrison, K. Murray, C. Otstott, B. Porter, R. Schrag, T. Uribe, J. Usher, and P. Yeh. A knowledge acquisition tool for course of action analysis. In IAAI, 2003.]]Google Scholar
- K. Barker, B. Porter, and P. Clark. A library of generic concepts for composing knowledge bases. In K-Cap'01, 2001.]] Google ScholarDigital Library
- H. Bunke, X. Jiang, and A. Kandel. On the minimum common supergraph of two graphs. Computing 65, 1, 2000.]] Google ScholarDigital Library
- H. Bunke and K. Shearer. A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters, 19, 1998.]] Google ScholarDigital Library
- H. Chalupsky. Ontomorph: a translation system for symbolic knowledge. In KR, 2000.]]Google Scholar
- P. Clark and B. Porter. KM: The knowledge machine. www.cs.utexas.edu/users/mfkb/km.]]Google Scholar
- P. Clark and B. W. Porter. Building concept representations from reusable components. In AAAI/IAAI, 1997.]]Google Scholar
- P. Clark, J. Thompson, and B. Porter. Knowledge patterns. In KR, 2000.]]Google Scholar
- D. Corbett and R. Woodbury. Unification over constraints in conceptual graphs. In ICCS, 1999.]] Google ScholarDigital Library
- B. Falkenhainer, K. D. Forbus, and D. Gentner. The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41(1), 1989.]] Google ScholarDigital Library
- K. Forbus and J. Usher. Sketching for knowledge capture: A progress report. In Intelligent User Interfaces, 2002.]] Google ScholarDigital Library
- D. Genest and M. Chein. An experiment in document retrieval using conceptual graphs. In ICCS, 1997.]] Google ScholarDigital Library
- D. Gentner. Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 1983.]]Google Scholar
- N. Guarino, C. Masolo, and G. Vetere. Ontoseek: Content-based access to the web. IEEE Intelligent Systems, 14(3), 1999.]] Google ScholarDigital Library
- D. B. Lenat and R. Guha. Building Large Knowledge-Based Systems. Addison-Wesley Publishing Company, 1990.]] Google ScholarDigital Library
- D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. An environment for merging and testing large ontologies. In KR, 2000.]]Google Scholar
- B. T. Messmer and H. Bunke. A network based approach to exact and inexact graph matching. Technical Report IAM 93-021, Institut fur Informatik, Universitat Bern, 1993.]]Google Scholar
- G. W. Mineau. Normalizing conceptual graphs. In P. Eklund, T. Nagle, J. Nagle, L. Gerhotz, and E. Horwood, editors, Current Directions in Conceptual Structure Research, 1992.]] Google ScholarDigital Library
- G. W. Mineau. Facilitating the creation of a multiple index on graph-described documents by transforming their descriptions. In CIKM, 1993.]] Google ScholarDigital Library
- S. Myaeng. Conceptual graphs as a framework for text retrieval. In P. Eklund, T. Nagle, J. Nagle, L. Gerhotz, and E. Horwood, editors, Current Directions in Conceptual Structure Research, 1992.]] Google ScholarDigital Library
- N. F. Noy and M. A. Musen. An algorithm for merging and aligning ontologies: automation and tool support. In AAAI, Workshop on Ontology Management, 1999.]]Google Scholar
- E. Salvat and M.-L. Mugnier. Sound and complete forward and backward chainingd of graph rules. In ICCS, 1996.]] Google ScholarDigital Library
- A. Sanfeliu and K. Fu. A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. on SMC, 13, 1983.]]Google Scholar
- L. Shapiro and R. Haralick. Structural descriptions and inexact matching. IEEE Trans. on PAMI, 3, 1981.]]Google Scholar
- J. F. Sowa. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley Publishing Company, 1984.]] Google ScholarDigital Library
- W. Tsai and K. Fu. Error-correcting isomorphisms of attributed relational graphs for pattern analysis. IEEE Trans. on SMC, 9, 1979.]]Google Scholar
- M. Willems. Projection and unification for conceptual graphs. In ICCS, 1995.]] Google ScholarDigital Library
- W. Woods. Conceptual indexing: A better way to organize knowledge. Technical Report TR-97-61, Sun Microsystems Laboratories, 1997.]] Google ScholarDigital Library
- P. Yeh, B. Porter, and K. Barker. Transformation rules for knowledge-based pattern matching. Technical Report UT-AI-TR-03-299, University of Texas at Austin, 2003.]]Google Scholar
- J. Zhong, H. Zhu, J. Li, and Y. Yu. Conceptual graph matching for semantic search. In ICCS, 2002.]] Google ScholarDigital Library
Index Terms
- Using transformations to improve semantic matching
Recommendations
Axiom-based ontology matching
K-CAP '05: Proceedings of the 3rd international conference on Knowledge captureThis paper introduces a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontology, but it can also be applied to lightweight ontology as a ...
Research on Ontology Matching Method Based on Description Logics Reasoning Mechanism
WISM '09: Proceedings of the 2009 International Conference on Web Information Systems and MiningNowadays the utility of domain ontologies is widely acknowledged in many area, such as information systems、software engineer、natrue language processing、artificial intelligence、electronic commerce and so on. Ontologies are enable to fulfill knowledge ...
Matching Semantic Web Services Using Learning Accuracy
SYNASC '09: Proceedings of the 2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific ComputingThe automatic discovery of suitable Web services for a given task is one of the key elements in implementing the Semantic Web vision. This paper presents a new matching algorithm for Semantic Web service discovery. Our matching algorithm allows for ...
Comments