Nonmonotonic reasoning for adaptive information filtering
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Ontology-Based Automatic Classification for the Web Pages: Design, Implementation and Evaluation
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
An intelligent search agent system for semantic information retrieval on the internet
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Toward an ontology-enhanced information filtering agent
ACM SIGMOD Record
Mapping lexical entries in a verbs database to WordNet senses
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Using Probabilistic Latent Semantic Analysis for Web Page Grouping
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Semantic similarity methods in wordNet and their application to information retrieval on the web
Proceedings of the 7th annual ACM international workshop on Web information and data management
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With the volume of information on the Internet growing at an exponential rate, the needs of users to have their search results effectively filtered is increasingly important. A problem with most of the current search engines is that they only search on the specified keyword, which may be present in only a limited number of pages. This paper examines how a tree threshold function can be used in an information filtering agent (IFA) to extend the original keyword search to cover other related words within the domain, creating a keyword weighted semantic tree. The examination in this paper also considers how the metrics of the tree structure (shape, size, weights) influence the choice of related words for use in the extended search and what advantage this has over traditional methods. Further, that using a reduced word tree, which has been pruned using the tree pruning algorithm produces a significant increase in the number of profitable results for the user. Using these factors the analysis demonstrates equal accuracy to the benchmark comparison IFA but with increased efficiency and only a slight increase in execution time.