Semantic Predications for Complex Information Needs in Biomedical Literature.
Semantic Predications for Complex Information Needs in Biomedical Literature.
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DOI：
10.1109/bibm.2011.23
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Thirunarayan K
中科院分区：
文献类型：
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作者：
Cameron D;Kavuluru R;Bodenreider O;Mendes PN;Sheth AP;Thirunarayan K
生物医学学科中出现的许多复杂的信息需求需要探索多个文档以获取信息。虽然传统的信息检索技术，返回一个单一的排名列表的文件是非常常见的这样的任务，他们可能并不总是足够的。主要问题是，排名列表通常会给用户带来很大的负担，让他们过滤掉不相关的文档。此外，用户必须直观地重新制定他们的搜索查询时，相关的文件还没有排名不高。此外，即使在已经选择了感兴趣的文档之后，也很少存在能够实现文档到文档转换的机制。在本文中，我们展示了实用的断言提取的生物医学文本（称为语义预测），以方便检索相关的文件，复杂的信息需求。我们的方法提供了一种替代查询重新制定建立一个框架，从一个文件过渡到另一个。我们使用2006年TREC基因组学跟踪的精确度和召回率指标来评估这种新的知识驱动的方法。
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.