Interactive Debugging of Knowledge Bases
Patrick Rodler

TL;DR
This paper introduces a complete, sound, and optimal interactive debugging approach for knowledge bases, involving domain experts in resolving logical inconsistencies while ensuring minimal and semantically correct repairs.
Contribution
It presents novel methods that enable interactive, minimal, and semantically accurate KB repairs, overcoming limitations of existing non-interactive systems.
Findings
Proposes complete and sound debugging algorithms.
Ensures minimal and semantically correct KB repairs.
Involves domain experts through automated queries.
Abstract
Many AI applications rely on knowledge about a relevant real-world domain that is encoded by means of some logical knowledge base (KB). The most essential benefit of logical KBs is the opportunity to perform automatic reasoning to derive implicit knowledge or to answer complex queries about the modeled domain. The feasibility of meaningful reasoning requires KBs to meet some minimal quality criteria such as logical consistency. Without adequate tool assistance, the task of resolving violated quality criteria in KBs can be extremely tough even for domain experts, especially when the problematic KB includes a large number of logical formulas or comprises complicated logical formalisms. Published non-interactive debugging systems often cannot localize all possible faults (incompleteness), suggest the deletion or modification of unnecessarily large parts of the KB (non-minimality), return…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Semantic Web and Ontologies · Logic, Reasoning, and Knowledge
