A personalizable agent for semantic taxonomy-based web search

Larry Kerschberg, Wooju Kim, Anthony Scime

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper addresses the problem of specifying Web searches and retrieving, filtering, and rating Web pages so as to improve the relevance and quality of hits, based on the user's search intent and preferences. We present a methodology and architecture for an agent-based system, called WebSifter II, that captures the semantics of a user's decision-oriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. Consulting a Web taxonomy agent such as WordNet helps refine the terms in the tree. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating.

Original languageEnglish
Pages (from-to)3-31
Number of pages29
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2564
Publication statusPublished - 2003 Dec 1

Fingerprint

Web Search
Taxonomies
Taxonomy
Semantics
Search engines
Query
Hits
Search Engine
Syntactics
Agent-based Systems
World Wide Web
WordNet
Websites
Categorical
Filtering
Transform
Target
Methodology
Term
Relevance

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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