Learning the heterogeneous bibliographic information network for literature-based discovery

Yakub Sebastian, Eu Gene Siew, Sylvester Olubolu Orimaye

Research output: Contribution to journalArticle

Abstract

This paper presents HBIN-LBD, a novel literature-based discovery (LBD) method that exploits the lexico-citation structures within the heterogeneous bibliographic information network (HBIN) graphs. Unlike other existing LBD methods, HBIN-LBD harnesses the metapath features found in HBIN graphs for discovering the latent associations between scientific papers published in otherwise disconnected research areas. Further, this paper investigates the effects of incorporating semantic and topic modeling components into the proposed models. Using time-sliced historical bibliographic data, we demonstrate the performance of our method by reconstructing two LBD hypotheses: the Fish Oil and Raynaud's Syndrome hypothesis and the Migraine and Magnesium hypothesis. The proposed method is capable of predicting the future co-citation links between research papers of these previously disconnected research areas with up to 88.86% accuracy and 0.89 F-measure.

Original languageEnglish
Pages (from-to)66-79
Number of pages14
JournalKnowledge-Based Systems
Volume115
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Fingerprint Dive into the research topics of 'Learning the heterogeneous bibliographic information network for literature-based discovery'. Together they form a unique fingerprint.

  • Cite this