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2014 | OriginalPaper | Chapter

Improving Search Engines’ Document Ranking Employing Semantics and an Inference Network

Authors : Christos Makris, Yannis Plegas, Giannis Tzimas, Emmanouil Viennas

Published in: Web Information Systems and Technologies

Publisher: Springer Berlin Heidelberg

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Abstract

The users search mainly diverse information from several topics and their needs are difficult to be satisfied from the techniques currently employed in commercial search engines and without intervention from the user. In this paper, a novel framework is presented for performing re-ranking in the results of a search engine based on feedback from the user. The proposed scheme combines smoothly techniques from the area of Inference Networks and data from semantic knowledge bases. The novelty lies in the construction of a probabilistic network for each query which takes as input the belief of the user to each result (initially, all are equivalent) and produces as output a new ranking for the search results. We have constructed an implemented prototype that supports different Web search engines and it can be extended to support any search engine. Finally extensive experiments were performed using the proposed methods depicting the improvement of the ranking of the search engines results.

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Metadata
Title
Improving Search Engines’ Document Ranking Employing Semantics and an Inference Network
Authors
Christos Makris
Yannis Plegas
Giannis Tzimas
Emmanouil Viennas
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Web Information Systems and Technologies

Print ISBN: 978-3-662-44299-9

Electronic ISBN: 978-3-662-44300-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-662-44300-2_9

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