Generating Explanations for Biomedical Queries
This work addresses the need for interpretable explanations in biomedical query systems, particularly for drug discovery, though it appears incremental as it builds on existing answer set programming methods.
The authors tackled the problem of generating explanations for biomedical queries by introducing novel mathematical models and algorithms using answer set programming, resulting in an implementation integrated into BIOQUERY-ASP and demonstrated with complex queries over multiple biomedical knowledge resources.
We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for biomedical queries, using answer set programming. We implement these algorithms and integrate them in BIOQUERY-ASP. We illustrate the usefulness of these methods with some complex biomedical queries related to drug discovery, over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY and ORPHADATA. To appear in Theory and Practice of Logic Programming (TPLP).