Concept Level Web Search Via Semantic Clustering

Authors:
Nian Yan;Deepak Khazanchi
Affiliations:
College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA;College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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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.