A Semantic Web data retrieval implementation with an adaptive model for supporting agent decision structures View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-03

AUTHORS

James V. Hansen, James B. McDonald, Conan C. Albrecht, Douglas L. Dean, Bonnie B. Anderson

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 uses 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 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. More... »

PAGES

5-16

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10660-006-0060-1

DOI

http://dx.doi.org/10.1007/s10660-006-0060-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038060013


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