Concept level web search via semantic clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Internet search engine techniques have evolved from simple web searching using categorization (e.g., Yahoo) to advanced page ranking algorithms (e.g., Google). However, the challenge for the next generation of search algorithms is not the quantity of search results, but identifying the most relevant pages based on a semantic understanding of user requirements. This notion of relevance is closely tied to the semantics associated with the term being searched. The ideal situation would be to represent results in an intuitive way that allows the user to view their search results in terms of concepts related to their search word or phrase rather than a list of ranked web pages. In this paper, we propose a semantic clustering approach that can be used to build a conceptual search engine.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
Pages806-812
Number of pages7
EditionPART 3
StatePublished - Dec 1 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Science, ICCS 2007
CountryChina
CityBeijing
Period5/27/075/30/07

Fingerprint

Web Search
Semantics
World Wide Web
Cluster Analysis
Search Engine
Clustering
Search engines
Categorization
Internet
Search Algorithm
Websites
Intuitive
Ranking
Requirements
Term
Concepts

Keywords

  • Conceptual search
  • Document clustering
  • Information retrieval
  • Search engine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yan, N., & Khazanchi, D. (2007). Concept level web search via semantic clustering. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings (PART 3 ed., pp. 806-812). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

Concept level web search via semantic clustering. / Yan, Nian; Khazanchi, Deepak.

Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. 2007. p. 806-812 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yan, N & Khazanchi, D 2007, Concept level web search via semantic clustering. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4489 LNCS, pp. 806-812, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 5/27/07.
Yan N, Khazanchi D. Concept level web search via semantic clustering. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 ed. 2007. p. 806-812. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
Yan, Nian ; Khazanchi, Deepak. / Concept level web search via semantic clustering. Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. 2007. pp. 806-812 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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