Path knowledge discovery: Association mining based on multi-category lexicons

Transdisciplinary research is a rapidly expanding part of science and engineering, demanding new methods for connecting results across fields. In biomedicine for example, modeling complex biological systems requires linking knowledge across multiple levels of science, from genes to disease. The move to multilevel research requires new strategies; in this paper we present path knowledge discovery, a novel methodology for linking published research findings. Path knowledge discovery consists of two integral tasks: 1) association path mining among concepts in a multipart lexicon that crosses disciplines, and 2) fine-granularity knowledge-based content retrieval along the path(s) to permit deeper analysis. Implementing this methodology has required development of innovative measures of association strength for pairwise associations, as well as the strength for sequences of associations, in addition to powerful lexicon-based association expansion to increase the scope of matching. In our discussions, we describe the validation of the methodology using a published heritability study from cognition research, and we obtain comparable results. We show how path knowledge discovery can greatly reduce a domain expert´s time (by several orders of magnitude) when searching and gathering knowledge from the published literature, and can facilitate derivation of interpretable results.