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Improving large-scale search engines with semantic annotations
Abstract Traditional search engines have become theTraditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries. In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results. Our work is supported by (1) the logs generated after query searching, (2) semantic annotations of queries and (3) semantic annotations of web pages. The algorithm makes use of this information to elaborate an appropriate ranking. To validate our approach we have implemented a system that can apply the algorithm to a particular search engine. Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it. © 2012 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved.
Abstractsub Traditional search engines have become theTraditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries. In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results. Our work is supported by (1) the logs generated after query searching, (2) semantic annotations of queries and (3) semantic annotations of web pages. The algorithm makes use of this information to elaborate an appropriate ranking. To validate our approach we have implemented a system that can apply the algorithm to a particular search engine. Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it. © 2012 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved.
Bibtextype article  +
Doi 10.1016/j.eswa.2012.10.042  +
Has author Fuentes-Lorenzo D. + , Fernandez N. + , Fisteus J.A. + , Sanchez L. +
Has extra keyword Clickthrough data + , Collaborative tagging + , Ranking algorithm + , Semantic annotation + , Semantic search + , Wikipedia + , Algorithms + , Search engine + , Websites + , Semantics +
Has keyword Click-through data + , Collaborative tagging + , Ranking algorithm + , Semantic annotation + , Semantic search + , Wikipedia +
Issn 9574174  +
Issue 6  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 2287–2296  +
Published in Expert Systems with Applications +
Title Improving large-scale search engines with semantic annotations +
Type journal article  +
Volume 40  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 7 November 2014 20:19:57  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 20:19:57  +
DateThis property is a special property in this wiki. 2013  +
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