Hypothesis Ranking Based on Semantic Event Similarities
Hypothesis Ranking Based on Semantic Event Similarities
复制标题
基于语义事件相似度的假设排序
DOI：
10.2197/ipsjtbio.4.9
复制
发表时间：
2011
影响因子：
--
通讯作者：
K. Uehara
中科院分区：
文献类型：
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作者：
Taiki Miyanishi;Kazuhiro Seki;K. Uehara
在生物医学领域技术进步的推动下，其文献规模一直在非常迅速地增长。因此，单个研究人员不可能理解和综合所有与他们兴趣相关的信息。因此，通过链接文献中独立描述的信息片段来发现隐藏的知识或假设是可以想象的。事实上，文献采掘界已经报道了这样的假设；其中一些甚至得到了实验的证实。本文主要关注假设排序，并研究了一种基于事件之间的语义相似性来确定合理假设的方法，这些事件之间的语义相似性导致各自的假设。我们的假设是，从语义相似的事件生成的假设更合理。我们开发了一个名为假设资源管理器的原型系统，并进行了评价性实验，通过与以前工作中经常采用的基于词频的方法进行比较，证明了我们方法的有效性。
Accelerated by the technological advances in the biomedical domain, the size of its literature has been growing very rapidly. As a consequence, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. Therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. In fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. This paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. Our assumption is that hypotheses generated from semantically similar events are more reasonable. We developed a prototype system called, Hypothesis Explorer, and conducted evaluative experiments through which the validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the previous work.