Keywords

adaptive learning, agents, ontology, semantic web

Abstract

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.

Original Publication Citation

Hansen, J.V., McDonald, J.B., Albrecht, C.C, Dean, D.L., Anderson, B.B. 2007. “A Semantic Web Data Retrieval Implementation with an Adaptive Model for Supporting Agent Decision Structures,” Electronic Commerce Research, Vol 7, pp. 5-17.

Document Type

Peer-Reviewed Article

Publication Date

2005

Publisher

Electronic Commerce Research

Language

English

College

Marriott School of Business

Department

Information Systems Management

University Standing at Time of Publication

Full Professor

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