Generating literature-based knowledge discoveries in life sciences using relationship associations

The life sciences have been a pioneering discipline for the field of knowledge discovery, since the literaturebased discoveries by Swanson three decades ago. Existing literature-based knowledge discovery techniques generally try to discover hitherto unknown associations of domain concepts based on associations that can be established from the literature. However, scientific facts are more often expressed as specific relationships between concepts and/or entities that have been established through scientific research. A pair of relationships that predicate the specific way in which one concept relates to another can be associated if one of the concepts from each relationship can be determined to be semantically equivalent; we call this a "relationship association". Then, by making the same assumption of the transitivity of association used by Swanson and others, we can generate a hypothetical relationship association by combining two relationship associations that have been extracted from a knowledge base. Here we describe an algorithm for generating potential knowledge discoveries in the form of new relationship associations that are implied but not actually stated, and we test the algorithm against a corpus of almost 5000 relationship associations that we have extracted in previous work from 392 semantic graphs representing research articles from MEDLINE.