  Literature-based knowledge discovery process identifies the important but
implicit relations among information embedded in published literature. Existing
techniques from Information Retrieval and Natural Language Processing attempt
to identify the hidden or unpublished connections between information concepts
within published literature, however, these techniques undermine the concept of
predicting the future and emerging relations among scientific knowledge
components encapsulated within the literature. Keyword Co-occurrence Network
(KCN), built upon author selected keywords (i.e., knowledge entities), is
considered as a knowledge graph that focuses both on these knowledge components
and knowledge structure of a scientific domain by examining the relationships
between knowledge entities. Using data from two multidisciplinary research
domains other than the medical domain, capitalizing on bibliometrics, the
dynamicity of temporal KCNs, and a Long Short Term Memory recurrent neural
network, this study proposed a framework to successfully predict the future
literature-based discoveries - the emerging connections among knowledge units.
Framing the problem as a dynamic supervised link prediction task, the proposed
framework integrates some novel node and edge-level features. Temporal
importance of keywords computed from both bipartite and unipartite networks,
communities of keywords, built upon genealogical relations, and relative
importance of temporal citation counts used in the feature construction
process. Both node and edge-level features were input into an LSTM network to
forecast the feature values for positive and negatively labeled non-connected
keyword pairs and classify them accurately. High classification performance
rates suggest that these features are supportive both in predicting the
emerging connections between scientific knowledge units and emerging trend
analysis.
