Semantic ranking and result visualization for life sciences publications
Semantic ranking and result visualization for life sciences publications
复制标题
生命科学出版物的语义排名和结果可视化
DOI：
10.1109/icde.2010.5447931
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
K. A. Ross
中科院分区：
文献类型：
--
作者：
Julia Stoyanovich;William Mee;K. A. Ross
在生命科学领域中，数量不断增加的数据和语义知识正在引起新的数据管理挑战。在本文中，我们着重于将语义维度添加到文献搜索中，这是科学研究的核心任务。我们将注意力集中在生命科学中最重要的书目来源PubMed上，并探索使用网格词汇中高质量语义注释的方法来对搜索结果进行排名。首先，我们开发了几个将搜索查询与文档注释相关联的排名功能的家庭。然后，我们为每个家庭提出了有效的自适应排名机制。我们还描述了一个基于二维天际线的可视化，可以与排名结合使用，以进一步改善用户与系统的相互作用，并演示如何适应有效地计算此类天际线。最后，我们通过用户研究评估了排名的有效性。
An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the semantic dimension to literature search, a central task in scientific research. We focus our attention on PubMed, the most significant bibliographic source in life sciences, and explore ways to use high-quality semantic annotations from the MeSH vocabulary to rank search results. We start by developing several families of ranking functions that relate a search query to a document's annotations. We then propose an efficient adaptive ranking mechanism for each of the families. We also describe a two-dimensional Skyline-based visualization that can be used in conjunction with the ranking to further improve the user's interaction with the system, and demonstrate how such Skylines can be computed adaptively and efficiently. Finally, we evaluate the effectiveness of our ranking with a user study.