Plausible reasoning over large health datasets: A novel approach to data analytics leveraging semantics

Abstract

Plausible reasoning is an interesting and viable approach for semantic data analytics as it provides a non-deterministic and exploratory approach to inferring new knowledge from large datasets. Plausible reasoning identifies meaningful associations between data elements by analyzing the underlying semantics of the data to identify knowledge that can assist in complex decision-making, especially when dealing with incomplete and noisy knowledge. In this work, we present a plausible reasoning approach that computerizes formal plausible patterns using semantic web methods for knowledge representation and reasoning over knowledge graphs to discover new knowledge from large datasets. We implemented six plausible patterns, representing three different types of semantic relationships, by developing PL-OWL as a plausible extension to the Web Ontology Language (OWL). We incorporated the plausible patterns within our plausible reasoning framework—SeDan (SEmantics-based Data Analytics) —to provide plausible reasoning-based query answering over knowledge graphs to generate new knowledge and provide answers to complex queries. We evaluated our plausible reasoning-based query answering approach by answering medical questions from BioASQ challenges using SeDan which incorporates the Semantic MEDLINE database, DrugBank, and Disease Ontology for supplementary semantic associations. Our experimental results show that our plausible reasoning method was able to expand the query answering coverage of the Semantic MEDLINE database by 37% to generate plausible answers to previously unanswered queries—importantly 88% of the plausible answers generated were clinically reasonable and verified by a domain expert.