UMBC ebiquity

Trust Based Knowledge Outsourcing for Semantic Web Agents

Authors: Li Ding, Lina Zhou, and Tim Finin

Book Title: Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence

Date: October 12, 2003

Abstract: The Semantic Web enables intelligent agents to ``outsource'' knowledge, extending and enhancing their limited knowledge bases. An open question is how agents can efficiently and effectively access the vast knowledge on the inherently open and dynamic Semantic Web. The problem is not that of {em finding} a source for desired information, but deciding which among many possibly inconsistent sources is most reliable. We propose an approach to agent knowledge outsourcing inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as reliable sources of information or services. We focus on two important practical issues: {it learning trust} and {it justifying trust}. An agent can learn trust relationships by reasoning about its direct interactions with other agents and about public or private reputation information, i.e., the aggregate trust evaluations of other agents. We use the term trust justification to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model. We describe the results of simulation experiments of the use and evolution of trust in multi-agent systems. Our experiments demonstrate that the use of explicit trust knowledge can significantly improve knowledge outsourcing performance. We also describe a collaborative trust justification technique that focuses on reducing search complexity, handling inconsistent knowledge, and avoiding error propagation.

Type: InProceedings

Pages: 379- 387

Tags: trust, semantic web, agent

Google Scholar: aDXwO6TYiz8J

Number of Google Scholar citations: 47 [show citations]

Number of downloads: 2790


Available for download as

size: 1797523 bytes

Related Projects:

Past Project

 Semantic Discovery: Discovering Complex Relationships in Semantic Web.