TY - JOUR
T1 - Learning the heterogeneous bibliographic information network for literature-based discovery
AU - Sebastian, Yakub
AU - Siew, Eu Gene
AU - Orimaye, Sylvester Olubolu
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Heterogeneous bibliographic information network
KW - Link prediction
KW - Literature-based discovery
UR - http://www.scopus.com/inward/record.url?scp=84995910778&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2016.10.015
DO - 10.1016/j.knosys.2016.10.015
M3 - Article
AN - SCOPUS:84995910778
SN - 0950-7051
VL - 115
SP - 66
EP - 79
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -