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.
@cite_10 , similar to our work, the question why is an atom @math in an answer set @math for an ASP program @math '' is studied. As an answer to this question, the authors of @cite_10 provide the rule in @math that supports @math with respect to @math ; whereas we compute shortest or @math different explanations (as a tree whose vertices are labeled by rules).
@cite_10
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This paper discusses the background, algorithms and implementation techniques to support programmers in 'debugging' logic programs under the answer set semantics. We first investigate what constitutes an error in such programs and which classes of errors exist. This is used to motivate techniques and algorithms that respectively eliminate certain classes of errors and explain how an error occurred and why it occurred. Finally, details of the IDEAS (Interactive Development and Evaluation tool for Answer Set Semantics) system are given, a prototype version of which implements all of the techniques described.