KGBReF: A Knowledge Graph based Biomedical Relation Extraction Framework

With the rapid development of bibliographical data of biomedical articles, it is hard for scientists to keep up with the most recent biomedical literatures. Biomedical relation extraction aims to uncover high-quality relations from biomedical literature with high accuracy and efficiency. Of the existing text mining tools and semantic web products for relation extraction, knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relations among them, has enriched information for human annotation and thus has a great potential for assisting the extraction of the new relations. In this paper, we propose a knowledge graph based biomedical relation extraction framework KGBReF and apply the framework to explore emotion-probiotic relations. A probiotics knowledge graph with 40, 442, 404 triples was built and candidate relations in totally 1,453 PubMed articles were further retrieved by reasoning and annotated. Further, the evidence levels of relations were retrieved and visualized. Finally, we got an evidenced emotion-probiotic relation graph. KGBReF demonstrates an effective reasoning based framework of relation extraction by defining top concepts only. The annotated relation associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests.