TY - GEN
T1 - Predicting future links between disjoint research areas using heterogeneous bibliographic information network
AU - Sebastian, Yakub
AU - Siew, Eu Gene
AU - Orimaye, Sylvester Olubolu
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Literature-based discovery aims to discover hidden connections between previously disconnected research areas. Heterogeneous bibliographic information network (HBIN) provides a latent, semi-structured, bibliographic information model to signal the potential connections between scientific papers. This paper introduces a novel literature-based discovery method that builds meta path features from HBIN network to predict co-citation links between previously disconnected literatures. We evaluated the performance of our method in predicting future co-citation links between fish oil and Raynaud’s syndrome papers. Our experimental results showed that HBIN meta path features could predict future co-citation links between these papers with high accuracy (0.851 F-Measure; 0.845 precision; 0.857 recall), outperforming the existing document similarity algorithms such as LDA, TF-IDF, and Bibliographic Coupling.
AB - Literature-based discovery aims to discover hidden connections between previously disconnected research areas. Heterogeneous bibliographic information network (HBIN) provides a latent, semi-structured, bibliographic information model to signal the potential connections between scientific papers. This paper introduces a novel literature-based discovery method that builds meta path features from HBIN network to predict co-citation links between previously disconnected literatures. We evaluated the performance of our method in predicting future co-citation links between fish oil and Raynaud’s syndrome papers. Our experimental results showed that HBIN meta path features could predict future co-citation links between these papers with high accuracy (0.851 F-Measure; 0.845 precision; 0.857 recall), outperforming the existing document similarity algorithms such as LDA, TF-IDF, and Bibliographic Coupling.
KW - Classification
KW - Co-citation link prediction
KW - Features
KW - Heterogeneous information network
KW - Literature-based discovery
UR - http://www.scopus.com/inward/record.url?scp=84945584250&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18032-8_48
DO - 10.1007/978-3-319-18032-8_48
M3 - Conference Paper published in Proceedings
AN - SCOPUS:84945584250
SN - 9783319180311
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 610
EP - 621
BT - Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
A2 - Cao, Tru
A2 - Lim, Ee-Peng
A2 - Ho, Tu-Bao
A2 - Zhou, Zhi-Hua
A2 - Motoda, Hiroshi
A2 - Cheung, David
PB - Springer-Verlag London Ltd.
T2 - 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Y2 - 19 May 2015 through 22 May 2015
ER -