A Personal Agent Application for the Semantic Web

Subhash Kumar, Anugeetha Kunjithapatham, Mithun Sheshagiri, Tim Finin, Anupam Joshi, Yun Peng, and R. Scott Cost

The Semantic Web is a vision to simplify and improve knowledge reuse on the Web. It is all set to alter the way humans benefit from the web from active interaction to somewhat passive utilization through the proliferation of software agents and in particular personal assistants that can better function and thrive on the Semantic Web than the conventional web. Agents can parse, understand and reason about information available on Semantic Web pages in an attempt to use it to meet users’ needs. Such personal assistants will be driven by rules , axioms and the internal model or profile that the agents have inside them for the user. An intrinsic and important pre-requisite for a personal assistant or rather any agent is to manipulate information available on the Semantic Web in the form of ontologies, axioms, and rules written in various semantic markup languages. In this paper, a model architecture for such a personal assistant dealing with real-world semantic markup is described. The agent reasons with semantic markup written in DAML+OIL, using the Java Expert System Shell (JESS) as the reasoning engine. This software assistant views information providers on the Semantic Web as recommender agents that have a limited view of the user’s preferences and provides a improved notion of personalization by collaborating with peer personal assistants (what are referred to as buddy agents) within communities that the user has identified as trusted parties to exchange information with. Collaboration is achieved through simple solicitation and recommendation of information with these buddy agents.


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