Using UMLS-based Re-Weighting Terms as a Query Expansion Strategy

2006 IEEE International Conference on Granular Computing Pub Date : 2006-05-10 DOI:10.1109/GRC.2006.1635786

Weizhong Zhu, X. Xu, Xiaohua Hu, I. Song, R. Allen

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引用次数: 22

Abstract

Search engines have significantly improved the efficiency of bio-medical literature searching. These search engines, however, still return many results that are irrelevant to the intention of a user's query. To improve precision and recall, various query expansion strategies are widely used. In this paper, we explore the three widely used query expansion strategies - local analysis, global analysis, and ontology-based term re- weighting across various search engines. Through experiments, we show that ontology-based term re-weighting works best. Term re-weighting reformulates queries with selection of key original query terms and re-weights these key terms and their associated synonyms from UMLS. The results of experiments show that with LUCENE and LEMUR, the average precision is enhanced by up to 20.3% and 12.1%, respectively, compared to baseline runs. We believe the principles of this term re-weighting strategy may be extended and utilized in other bio-medical domains. users and suggest the user to refine the original query. In this research, three query expansion strategies - local analysis, global analysis, and ontology-based term re-weighting - integrated with the UMLS (Unified Medical Language System) are compared. These methods are applied to the Ad Hoc Retrieval task of the TREC 2004 Genomics task.

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使用基于uml的重加权词作为查询扩展策略

搜索引擎极大地提高了生物医学文献检索的效率。然而，这些搜索引擎仍然返回许多与用户查询意图无关的结果。为了提高查准率和查全率，各种查询扩展策略被广泛使用。在本文中，我们探讨了三种广泛使用的查询扩展策略——局部分析、全局分析和基于本体的术语重加权。实验表明，基于本体的词重加权方法效果最好。术语重加权通过选择关键的原始查询术语来重新定义查询，并从UMLS中重新加权这些关键术语及其相关同义词。实验结果表明，LUCENE和LEMUR的平均精度比基线分别提高了20.3%和12.1%。我们认为，这一术语重新加权策略的原则可以扩展并应用于其他生物医学领域。并建议用户对原始查询进行细化。在本研究中，比较了三种查询扩展策略-局部分析、全局分析和基于本体的术语重加权-与统一医学语言系统(UMLS)的集成。这些方法应用于TREC 2004基因组学任务的Ad Hoc检索任务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2006 IEEE International Conference on Granular Computing

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