期刊
JOURNAL OF INFORMETRICS
卷 9, 期 4, 页码 686-703出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.joi.2015.06.004
关键词
Literature based discovery; Named entity recognition; Relation extraction; Semantic path analysis; Semantic relatedness score
资金
- Bio-Synergy Research Project of the Ministry of Science, ICT, and Future Planning through the National Research Foundation [NRF-2013M3A9C4078138]
- project Research on Knowledge Organization and Service Innovation in the Big Data Environments - National Natural Science Foundation of China [71173249]
The enormous amount of biomedicine's natural-language texts creates a daunting challenge to discover novel and interesting patterns embedded in the text corpora that help biomedical professionals find new drugs and treatments. These patterns constitute entities such as genes, compounds, treatments, and side effects and their associations that spread across publications in different biomedical specialties. This paper proposes SemPathFinder to discover previously unknown relations in biomedical text. SemPathFinder overcomes the problems of Swanson's ABC model by using semantic path analysis to tell a story about plausible connections between biological terms. Storytelling-based semantic path analysis can be viewed as relation navigation for bio-entities that are semantically close to each other, and reveals insight into how a series of entity pairs is organized, and how it can be harnessed to explain seemingly unrelated connections. We apply SemPathFinder for two well-known use cases of Swanson's ABC model, and the experimental results show that SemPathFinder detects all intermediate terms except for one and also infers several interesting new hypotheses. (C) 2015 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据