Efficient argumentation over ontology correspondences

ABSTRACT

The ability to communicate is one of the key capabilities of an agent within a Multi-Agent System. In open environments, where agents are likely to use heterogeneous ontologies, it is necessary to use formally defined ontologies [1] to support communication. However, this requires either a shared ontology, or a set of correspondences (alignment) that map semantically related entities from one ontology to another. The open nature of such environments results in this being unlikely due to few a priori assumptions being made about the agents present; thus requiring techniques for dynamic alignment and reconciliation. To dynamically reconcile heterogeneous ontologies, agents need to be able to agree on an acceptable alignment between their ontologies. Various approaches attempt to resolve ontological mismatches in open environments [8, 4], using negotiation approaches that search the space of alignments to find a mutually acceptable set of correspondences. However, this search can become prohibitively costly when negotiation mechanisms such as argumentation are involved, reaching II^(p)₂-complete [5]. Hence, it is important to reduce the search space before the argumentation process takes place. The Meaning-based Argumentation approach [8] allows two agents to dynamically and automatically reach consensus by arguing over a set of candidate mappings (or correspondences) obtained from a mapping repository. We have explored the use of Ontology Modularization as a filtering mechanism for reducing the number of candidate mappings, by isolating only those mappings that are relevant to the communication, and hence reducing the size of the search space for the argumentation process.

REFERENCES

• 1: T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American, 284(5), 2001.
• 2: B. Cuenca Grau, I. Horrocks, Y. Kazakov, and U. Sattler. Modular reuse of ontologies: Theory and practice. JAIR, 31:273--318, 2008.
• 3: Paul Doran , Valentina Tamma , Luigi Iannone, Ontology module extraction for ontology reuse: an ontology engineering perspective, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321451]
• 4: Cassia Trojahn , Paulo Quaresma , Renata Vieira, Conjunctive queries for ontology based agent communication in MAS, Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, May 12-16, 2008, Estoril, Portugal
• 5: P. E. Dunne and T. J. M. Bench-Capon. Complexity in Value-Based Argument Systems. In J. J. Alferes and J. A. Leite, editors, JELIA, volume 3229 of LNCS, pages 360--371. Springer, 2004.
• 6: Jérôme Euzenat , Pavel Shvaiko, Ontology Matching, Springer-Verlag New York, Inc., Secaucus, NJ, 2007
• 7: J. Euzenat and P. Valtchev. Similarity-based ontology alignment in OWL-Lite. In ECAI-04, 2004.
• 8: Loredana Laera , Ian Blacoe , Valentina Tamma , Terry Payne , Jerôme Euzenat , Trevor Bench-Capon, Argumentation over ontology correspondences in MAS, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, May 14-18, 2007, Honolulu, Hawaii [doi>10.1145/1329125.1329400]
• 9: Julian Seidenberg , Alan Rector, Web ontology segmentation: analysis, classification and use, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135785]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Experimentation

Keywords:
argumentation, modularization, ontology alignment