文章基本信息

标题：Hypothesis Ranking Based on Semantic Event Similarities
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作者：Taiki Miyanishi ; Kazuhiro Seki ; Kuniaki Uehara 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2011
卷号：6
期号：3
页码：803-814
DOI：10.11185/imt.6.803
出版社：Information and Media Technologies Editorial Board
摘要：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.