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

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Web Information Systems and Technologies (WEBIST 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 189))

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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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Acknowledgements

This research has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)-Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.

This research has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)-Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.

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Correspondence to Yannis Plegas .

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Makris, C., Plegas, Y., Tzimas, G., Viennas, E. (2014). Improving Search Engines’ Document Ranking Employing Semantics and an Inference Network. In: Krempels, KH., Stocker, A. (eds) Web Information Systems and Technologies. WEBIST 2013. Lecture Notes in Business Information Processing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44300-2_9

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44299-9

  • Online ISBN: 978-3-662-44300-2

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