Essential Pages

• Published in: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Vol. 1; pp. 173 - 182
• Main Authors: Swaminathan, Ashwin, Mathew, Cherian V., Kirovski, Darko
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 15.09.2009; IEEE
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning; coverage; Information systems > Information retrieval; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Evaluation of retrieval results; Internet; Knowledge engineering; learning queries; Measurement; Optimization; Prototypes; Redundancy; redundancy elimination; Search engines; Semantics; Time-frequency analysis; Web page ranking; Web search; coverage; redundancy elimination; learning queries; Web page ranking; Web search
• ISBN: 0769538010; 9780769538013
• DOI: 10.1109/WI-IAT.2009.33

Results to Web search queries are ranked using heuristics that typically analyze the global link topology, user behavior, and content relevance. We point to a particular inefficiency of such methods: information redundancy. In queries where learning about a subject is an objective, modern search engines return relatively unsatisfactory results as they consider the query coverage by each page individually, not a set of pages as a whole. We address this problem using essential pages. If we denote as $\mathbb{S}_Q$ the total knowledge that exists on the Web about a given query $Q$, we want to build a search engine that returns a set of essential pages $E_Q$ that maximizes the information covered over $\mathbb{S}_Q$. We present a preliminary prototype that optimizes the selection of essential pages; we draw some informal comparisons with respect to existing search engines; and finally, we evaluate our prototype using a blind-test user study.