Improving large-scale search engines with semantic annotations
|Improving large-scale search engines with semantic annotations|
|Author(s)||Fuentes-Lorenzo D., Fernandez N., Fisteus J.A., Sanchez L.|
|Published in||Expert Systems with Applications|
|Keyword(s)||Click-through data, Collaborative tagging, Ranking algorithm, Semantic annotation, Semantic search, Wikipedia (Extra: Clickthrough data, Collaborative tagging, Ranking algorithm, Semantic annotations, Semantic search, Wikipedia, Algorithms, Search engines, Websites, Semantics)|
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Improving large-scale search engines with semantic annotations is a 2013 journal article written in English by Fuentes-Lorenzo D., Fernandez N., Fisteus J.A., Sanchez L. and published in Expert Systems with Applications.
Traditional 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.
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