Investigating plausible reasoning over knowledge graphs for semantics-based health data analytics

Plausible reasoning reflects the 'plasticity' element of human reasoning, which, by leveraging the semantics of relevant concepts, allows dealing with incomplete data during decision making. We propose the SEmantics-based Data ANalytics (SeDan) framework that integrates plausible reasoning with expressive, fine-grained biomedical ontologies. Using this framework, an unresolvable query can be rewritten to explore the semantic knowledge graph and infer new knowledge. While the gained insights may be plausible, i.e., not supported by crisp deductive reasoning, they may still aid complex medical decision making by recommending plausible solutions. In this paper, we investigate the efficiency of SeDan in a real-world medical setting to pose intelligent medical queries from BioASQ challenges over the MEDLINE database. Experimental results show that SeDan can expand the query answering coverage by resolving up to 45% of initially unresolvable queries. The correctness of the inferred answers, as well as the underlying plausible reasoning processes, was verified by a domain expert.