SemPathFinder: Semantic path analysis for discovering publicly unknown knowledge

• The present paper proposes a new LBD system, called SemPathFinder, which provides semantic path analysis that enables storytelling for plausible hypothesis generation.
• The paper adopts advanced text mining techniques such as named entity recognition and relation extraction to create a knowledge graph.
• The paper utilizes UMLS to improve accuracy of extraction and obtains semantics of extracted entities and relations.
• The paper explores several different ranking approaches including semantic related scores for spotting plausible new hypotheses.

The enormous amount of biomedicine's natural-language texts creates a daunting challenge to discover novel and interesting patterns embedded in the text corpora that help biomedical professionals find new drugs and treatments. These patterns constitute entities such as genes, compounds, treatments, and side effects and their associations that spread across publications in different biomedical specialties. This paper proposes SemPathFinder to discover previously unknown relations in biomedical text. SemPathFinder overcomes the problems of Swanson's ABC model by using semantic path analysis to tell a story about plausible connections between biological terms. Storytelling-based semantic path analysis can be viewed as relation navigation for bio-entities that are semantically close to each other, and reveals insight into how a series of entity pairs is organized, and how it can be harnessed to explain seemingly unrelated connections. We apply SemPathFinder for two well-known use cases of Swanson's ABC model, and the experimental results show that SemPathFinder detects all intermediate terms except for one and also infers several interesting new hypotheses.