Author(s): Hossein Mohammadhassanzadeh, Samina Abidi, William Van Woensel, Syed Sibte Raza Abidi
Abstract: Plausible reasoning is the manifestation of the “plasticity” element of human reasoning, which allows dealing with incomplete data and still discover unknown associations by leveraging semantics of concepts. We proposed SEmantics-based Data ANalytics framework (SeDan) that integrates plausible reasoning with fine-grained biomedical ontologies. Using this framework, an initial query with no answer can be transformed to an expanded version that effectively infers new knowledge. In this paper, we investigate the efficiency of SeDan in a real-world medical setting by using the framework to pose intelligent medical queries form BioASQ challenges over the Semantic MEDLINE database. We have developed a Semantic Web-based framework that stores data from databases into an RDF storage and the semantics from two biomedical OWL ontologies conduct the query rewriting. Experimental results show SeDan can expand the query answering coverage of SemMedDB by resolving up to 45% (depending on the type of the questions) of the initially unanswered questions. The correctness of the plausibly inferred answers was verified by a domain expert.
Keywords: Plausible Reasoning; Query Expansion; Semantic Web Reasoning; Semantic Analytics