Paper 5

Hypothesis Discovery Exploiting Closed Chains of Relations

Authors: Kazuhiro Seki

Volume 22 (2015)

Abstract

The ever-growing literature in biomedicine makes it virtually impossible for individuals to grasp all the information relevant to their interests. Since even experts’ knowledge is limited, important associations among key biomedical concepts may remain unnoticed in the flood of information. Discovering those hidden associations is called hypothesis discovery or literature-based discovery. This paper propose an approach to this problem taking advantage of a closed, triangular chain of relations extracted from the existing literature. We consider such chains of relations as implicit rules to generate explanatory hypotheses. The hypotheses generated from the implicit rules are then compared with newer knowledge for assessing their validity and, if validated, they are served as positive examples for learning a regression model to rank hypotheses. As a proof of concept, the proposed framework is empirically evaluated on real-world knowledge extracted from the biomedical literature. The results demonstrate that the framework is able to produce legitimate hypotheses and that the proposed ranking approach is more eective than the previous work.