Learning the heterogeneous bibliographic information network for literature-based discovery

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• 作者：Yakub Sebastian ; ^a ; ^{yakub.sebastian@monash.edu" class="auth_mail" title="E-mail the corresponding author} ; Eu-Gene Siew^b ; ^{siew.eu-gene@monash.edu" class="auth_mail" title="E-mail the corresponding author} ; Sylvester Olubolu Orimaye^a ; ^{sylvester.orimaye@monash.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Literature-based discovery ; Heterogeneous bibliographic information network ; Link prediction
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：115
• 期：Complete
• 页码：66-79
• 全文大小：2795 K

文摘

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.