A Semantic Web Data Retrieval Implementation with an Adaptive Model for Supporting Agent Decision Structures
Electronic Commerce Research, Vol. 7, pp. 5-16, 2007
25 Pages Posted: 5 Jun 2009 Last revised: 6 Nov 2014
Date Written: 2007
This paper proposes an adaptive learning approach that yields decision models that can be applied by a transactions agent. This model can learn effectively with a variety of data distributions. This research used the Semantic Web as a data access approach. The Semantic Web is a method that sellers can use to publish semantically meaningful information on Websites so that automated applications can reliably access that information. We implemented a Semantic Web composed of 30 vendors' Web pages and a spider to search those pages to obtain product and vendor information. This information was used to train a learning agent, which then provided a decision model to a transaction agent.
Keywords: adaptive learning, agents, ontology, semantic web
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