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.
The most recent work related to explanation generation in ASP are @cite_10 @cite_13 @cite_1 @cite_31 @cite_35 @cite_34 , in the context of debugging ASP programs. Among those, @cite_13 studies why a program does not have an answer set, and @cite_1 @cite_35 studies why a set of atoms is not an answer set. As we study the problem of explaining the reasons why atoms are in the answer set, our work differs from those two work.
@cite_35 @cite_10 @cite_1 @cite_31 @cite_34 @cite_13
2138596350 50549667 2037950528 2171836055
The paper introduces the notion of offline justification for answer set programming (ASP). Justifications provide a graph-based explanation of the truth value of an atom with respect to a given answer set. The paper extends also this notion to provide justification of atoms during the computation of an answer set (on-line justification) and presents an integration of online justifications within the computation model of SMODELS. Offline and online justifications provide useful tools to enhance understanding of ASP, and they offer a basic data structure to support methodologies and tools for debugging answer set programs. A preliminary implementation has been developed in . In recent years, Answer Set Programming ASP, logic programming under the stable model or answer set semantics, has seen several extensions by generalizing the notion of an atom in these programs: be it aggregate atoms, HEX atoms, generalized quantifiers, or abstract constraints, the idea is to have more complicated satisfaction patterns in the lattice of Herbrand interpretations than traditional, simple atoms. In this paper we refer to any of these constructs as generalized atoms. It is known that programs with generalized atoms that have monotonic or antimonotonic satisfaction patterns do not increase complexity with respect to programs with simple atoms if satisfaction of the generalized atoms can be decided in polynomial time under most semantics. It is also known that generalized atoms that are nonmonotonic being neither monotonic nor antimonotonic can, but need not, increase the complexity by one level in the polynomial hierarchy if non-disjunctive programs under the FLP semantics are considered. In this paper we provide the precise boundary of this complexity gap: programs with convex generalized atom never increase complexity, while allowing a single non-convex generalized atom under reasonable conditions always does. We also discuss several implications of this result in practice. Answer-set programming (ASP) has emerged as a declarative programming paradigm where problems are encoded as logic programs, such that the so-called answer sets of theses programs represent the solutions of the encoded problem. The efficiency of the latest ASP solvers reached a state that makes them applicable for problems of practical importance. Consequently, problems from many different areas, including diagnosis, data integration, and graph theory, have been successfully tackled via ASP. In this work, we present such ASP-encodings for problems associated to abstract argumentation frameworks (AFs) and generalisations thereof. Our encodings are formulated as fixed queries, such that the input is the only part depending on the actual AF to process. We illustrate the functioning of this approach, which is underlying a new argumentation system called ASPARTIX in detail and show its adequacy in terms of computational complexity. Answer-set programming (ASP) is widely recognised as a viable tool for declarative problem solving. However, there is currently a lack of tools for developing answer-set programs. In particular, providing tools for debugging answer-set programs has recently been identified as a crucial prerequisite for a wider acceptance of ASP. In this paper, we introduce a meta-programming technique for debugging in ASP. The basic question we address is why interpretations expected to be answer sets are not answer sets of the program to debug. We thus deal with finding semantical errors of programs. The explanations provided by our method are based on an intuitive scheme of errors that relies on a recent characterisation of the answer-set semantics. Furthermore, as we are using a meta-programming technique, debugging queries are expressed in terms of answer-set programs themselves, which has several benefits: For one, we can directly use ASP solvers for processing debugging queries. Indeed, our technique can easily be implemented, and we devised a corresponding prototype debugging system. Also, our approach respects the declarative nature of ASP, and the capabilities of the system can easily be extended to incorporate differing debugging features.