Projects per year
This paper aims to develop a domain-independent system for repairing faulty Datalog-like theories by combining three existing techniques: abduction, belief revision and conceptual change. Accordingly, the proposed system is named the ABC repair system. Given an observed assertion and a current theory, abduction adds axioms which represent the simplest and most likely explanation. Belief revision incorporates a new piece of information which conflicts with the input theory by deleting axioms. Conceptual change uses the reformation algorithm for blocking unwanted proofs or unblocking wanted proofs. The former two techniques change an axiom as a whole, while reformation changes the language in which the theory is written. These three techniques are complementary: abduction adds new axioms, belief revision deletes conflicting axioms, while reformation changes the language of the theory. But they have not previously been combined into one system. We are working on aligning these three techniques in the ABC repair system, which is capable of repairing logical theories with better quality than individual techniques. Datalog is used as the underlying logic of theories in this paper, but the proposed system has the potential to be adapted to theories in other logics.
|Title of host publication||10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management|
|Place of Publication||Seville, Spain|
|Number of pages||8|
|Publication status||Published - Sep 2018|
|Event||10th International Conference on Knowledge Engineering and Ontology Development - Seville, Spain|
Duration: 18 Sep 2018 → 20 Sep 2018
|Conference||10th International Conference on Knowledge Engineering and Ontology Development|
|Abbreviated title||KEOD 2018|
|Period||18/09/18 → 20/09/18|
FingerprintDive into the research topics of 'ABC Repair System for Datalog-like Theories'. Together they form a unique fingerprint.
- School of Informatics - Professor
- Artificial Intelligence and its Applications Institute
- Data Science and Artificial Intelligence
Person: Academic: Research Active