Discovering relational knowledge from two disjoint sets of literatures using inductive Logic Programming

Literature-based discovery for hypothesis generation is a subarea of text mining that aims to discover novel or previously-unknown knowledge from two complementary but disjoint (CBD) sets of literatures. The discovery approach is based on Swanson's discovery models where indirect connections between two disjoint sets of literatures A and C could be found through a set of common terms B extracted from A and C. In this paper, we report an application of an inductive logic programming (ILP), specifically the WARMR algorithm, to the field of literature-based discovery. The application extends Swanson's closed discovery model to uncover potentially meaningful knowledge in forms of relational frequent patterns that may exist after the connections between the two sets of literatures are found. We conducted an experiment between two pairs of topics: Raynaud's disease and fish oils, and Down syndrome and cell polarity. The experimental results demonstrate that our method can be used to enhance a literature-based discovery approach by providing potentially meaningful knowledge in addition to the indirect connections.