MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs
This work addresses therapeutic substitution of antibiotics for medical professionals, but it is incremental as it builds on existing semantic similarity methods with domain-specific enhancements.
The authors tackled the problem of measuring semantic similarity between antibiotics in biomedical knowledge graphs, resulting in MedSim, which achieved statistically significant improvement over existing methods on a dataset of 528 antibiotic pairs scored by doctors.
We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics. Besides hierarchy and corpus of KGs, MedSim further interprets medicine characteristics by constructing multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic pairs scored by doctors is applied for evaluation and MedSim has produced statistically significant improvement over other semantic similarity methods. Furthermore, some promising applications of MedSim in drug substitution and drug abuse prevention are presented in case study.