Essie: A concept-based search engine for structured biomedical text
Essie: A concept-based search engine for structured biomedical text
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DOI：
10.1197/jamia.m2233
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发表时间：
2007-05-01
影响因子：
6.4
通讯作者：
Demner-Fushman, Dina
中科院分区：
文献类型：
--
作者：
Ide, Nicholas C.;Loane, Russell F.;Demner-Fushman, Dina
本文描述了在Essie搜索引擎中实现的算法，该搜索引擎目前正在为国家医学图书馆的几个网站提供服务。Essie是一个基于短语的搜索引擎，具有术语和概念查询扩展和概率相关性排序功能。Essie的设计灵感来自于这样一种观察，即查询术语通常在概念上与文档中的术语相关，而实际上并没有出现在文档文本中。采用2003年和2006年文本检索会议（TREC）基因组学轨道的数据和标准评价方法对Essie的表现进行了评价。Essie是2003年TREC Genomics跟踪中表现最好的搜索引擎，其结果可与2006年TREC Genomics跟踪任务中排名最高的系统相媲美。Essie表明，利用文档结构、短语搜索和基于概念的查询扩展的明智组合是生物医学领域信息检索的有效方法。
This article describes the algorithms implemented in the Essie search engine that is currently serving several Web sites at the National Library of Medicine. Essie is a phrase-based search engine with term and concept query expansion and probabilistic relevancy ranking. Essie's design is motivated by an observation that query terms are often conceptually related to terms in a document, without actually occurring in the document text. Essie's performance was evaluated using data and standard evaluation methods from the 2003 and 2006 Text REtrieval Conference (TREC) Genomics track. Essie was the best-performing search engine in the 2003 TREC Genomics track and achieved results comparable to those of the highest-ranking systems on the 2006 TREC Genomics track task. Essie shows that a judicious combination of exploiting document structure, phrase searching, and concept based query expansion is a useful approach for information retrieval in the biomedical domain.