Semantic Predications for Complex Information Needs in Biomedical Literature.

Delroy Cameron, Ramakanth Kavuluru, Olivier Bodenreider, Pablo N Mendes, Amit P Sheth, Krishnaprasad Thirunarayan
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Abstract

Many complex information needs that arise in biomedical disciplines require exploring multiple documents in order to obtain information. While traditional information retrieval techniques that return a single ranked list of documents are quite common for such tasks, they may not always be adequate. The main issue is that ranked lists typically impose a significant burden on users to filter out irrelevant documents. Additionally, users must intuitively reformulate their search query when relevant documents have not been not highly ranked. Furthermore, even after interesting documents have been selected, very few mechanisms exist that enable document-to-document transitions. In this paper, we demonstrate the utility of assertions extracted from biomedical text (called semantic predications) to facilitate retrieving relevant documents for complex information needs. Our approach offers an alternative to query reformulation by establishing a framework for transitioning from one document to another. We evaluate this novel knowledge-driven approach using precision and recall metrics on the 2006 TREC Genomics Track.

生物医学文献中复杂信息需求的语义预测。
生物医学学科中出现的许多复杂信息需求都需要探索多个文档才能获得信息。虽然传统的信息检索技术通常会返回一个排序的文档列表,但对于这类任务来说,这种技术并不总是足够的。主要问题在于,排序列表通常会给用户带来很大的负担,需要过滤掉不相关的文档。此外,当相关文档排名不高时,用户必须凭直觉重新提出搜索查询。此外,即使在感兴趣的文档被选中后,也很少有机制能实现文档到文档的转换。在本文中,我们展示了从生物医学文本中提取的断言(称为语义谓词)在促进检索复杂信息需求的相关文档方面的效用。我们的方法通过建立一个从一个文档过渡到另一个文档的框架,提供了一种替代查询重构的方法。我们在 2006 年 TREC 基因组学赛道上使用精确度和召回率指标对这种新颖的知识驱动方法进行了评估。
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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