Knowledge discovery in web-directories: Finding term-relations to build a business ontology

Sandip Debnath, Tracy Mullen, Arun Upneja, C. Lee Giles

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

The Web continues to grow at a tremendous rate. Search engines find it increasingly difficult to provide useful results. To manage this explosively large number of Web documents, automatic clustering of documents and organising them into domain dependent directories became very popular. In most cases, these directories represent a hierarchical structure of categories and sub-categories for domains and sub-domains. To fill up these directories with instances, individual documents are automatically analysed and placed into them according to their relevance. Though individual documents in these collections may not be ranked efficiently, combinedly they provide an excellent knowledge source for facilitating ontology construction in that domain. In (mainly automatic) ontology construction steps, we need to find and use relevant knowledge for a particular subject or term. News documents provide excellent relevant and up-to-date knowledge source. In this paper, we focus our attention in building business ontologies. To do that we use news documents from business domains to get an up-to-date knowledge about a particular company. To extract this knowledge in the form of important "terms" related to the company, we apply a novel method to find "related terms" given the company name. We show by examples that our technique can be successfully used to find "related terms" in similar cases.

Original languageEnglish (US)
Pages (from-to)188-197
Number of pages10
JournalLecture Notes in Computer Science
Volume3590
StatePublished - Oct 24 2005
Event6th International Conference on E-Commerce and Web Technologies, EC-Web 2005 - Copenhagen, Denmark
Duration: Aug 23 2005Aug 26 2005

Fingerprint

Knowledge Discovery
Data mining
Ontology
Term
Industry
Search engines
Hierarchical Structure
Search Engine
World Wide Web
Continue
Business
Knowledge
Clustering
Dependent

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Knowledge discovery in web-directories : Finding term-relations to build a business ontology. / Debnath, Sandip; Mullen, Tracy; Upneja, Arun; Giles, C. Lee.

In: Lecture Notes in Computer Science, Vol. 3590, 24.10.2005, p. 188-197.

Research output: Contribution to journalConference article

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