Cameron Delroy, Kavuluru Ramakanth, Bodenreider Olivier, Mendes Pablo N, Sheth Amit P, Thirunarayan Krishnaprasad
Kno.e.sis Center, Wright State University, Dayton, OH 45435, USA.
National Library of Medicine, Bethesda MD 20894, USA.
IEEE Int Conf Bioinform Biomed Workshops. 2011;2011:512-519. doi: 10.1109/BIBM.2011.23.
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.
生物医学领域中出现的许多复杂信息需求都需要查阅多篇文档才能获取信息。虽然对于此类任务,返回单个文档排序列表的传统信息检索技术很常见,但它们可能并不总是足够的。主要问题在于,排序列表通常给用户带来很大负担,需要他们筛选出不相关的文档。此外,当相关文档排名不高时,用户必须直观地重新表述他们的搜索查询。而且,即使选择了感兴趣的文档,也很少有机制能够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)在促进为复杂信息需求检索相关文档方面的效用。我们的方法通过建立一个从一篇文档过渡到另一篇文档的框架,为查询重新表述提供了一种替代方法。我们在2006年TREC基因组学跟踪项目中使用精确率和召回率指标对这种新颖的知识驱动方法进行了评估。