Improving large-scale search engines with semantic annotations

被引：4

作者：

Fuentes-Lorenzo, Damaris ^{[1
]}

Fernandez, Norberto ^{[1
]}

Fisteus, Jesus A. ^{[1
]}

Sanchez, Luis ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Madrid 28911, Spain

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 06期

关键词：

Semantic annotation; Semantic search; Wikipedia; Click-through data; Ranking algorithm; Collaborative tagging; INFORMATION-RETRIEVAL;

D O I：

10.1016/j.eswa.2012.10.042

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：2287 / 2296

页数：10

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