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.
Bibliographical noteFunding Information:
This work was supported by the Bio-Synergy Research Project ( NRF-2013M3A9C4078138 ) of the Ministry of Science, ICT, and Future Planning through the National Research Foundation.
This research is also partially sponsored by the project titled Research on Knowledge Organization and Service Innovation in the Big Data Environments funded by the National Natural Science Foundation of China (No. 71173249 ).
© 2015 Elsevier Ltd.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Library and Information Sciences