Hypothesis generation from text based on co-evolution of biomedical concepts

Hypothesis generation (HG) refers to the task of mining meaningful implicit association between disjoint biomedical concepts. The majority of prior studies have focused on uncovering these implicit linkages from static snapshots of the corpus, thereby largely ignoring the temporal dynamics of medical concepts. More recently, a few initial studies attempted to overcome this issue by modelling the temporal change of concepts from natural language text. However, they still fail to leverage the evolutionary features of concepts from contemporary knowledge-bases (KB's) such as semantic lexicons and ontologies. In practice such KB's contain up-to-date information that is important to incorporate, especially, in highly evolving domains such as biomedicine. Furthermore, considering the complementary strength of these sources of information - corpus and ontology - a few natural questions arise: Can joint modelling of (co)evolutionary dynamics from these resources aid in encoding the temporal features at a granular level? Can the mutual evolution between these intertwined resources lead to better predictive effects? To answer these questions, in this study, we present a novel HG framework that unearths the latent associations between concepts by modeling their co-evolution across complementary sources of information. More specifically, the proposed approach adopts a shared temporal matrix factorization framework that models the co-evolution of concepts across both corpus and KB. Extensive experiments on the largest available biomedical corpus validates the effectiveness of the proposed approach.