Learning the heterogeneous bibliographic information network for literature-based discovery
Document Type
Article
Publication Title
Knowledge-Based Systems
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.
First Page
66
Last Page
79
DOI
10.1016/j.knosys.2016.10.015
Publication Date
1-1-2017
Recommended Citation
Sebastian, Yakub; Siew, Eu Gene; and Orimaye, Sylvester Olubolu, "Learning the heterogeneous bibliographic information network for literature-based discovery" (2017). Global Population Health Faculty Publications. 16.
https://doi.org/10.1016/j.knosys.2016.10.015
https://collections.uhsp.edu/global-population-health_pubs/16