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Many AI applications rely on knowledge encoded in a locigal knowledge base(KB). The most essential benefit of such logical KBs is the opportunity toperform automatic reasoning which however requires a KB to meet some minimalquality criteria such as consistency. Without adequate tool assistance, thetask of resolving such violated quality criteria in a KB can be extremely hard,especially when the problematic KB is large and complex. To this end,interactive KB debuggers have been introduced which ask a user queries whethercertain statements must or must not hold in the intended domain. The givenanswers help to gradually restrict the search space for KB repairs.  Existing interactive debuggers often rely on a pool-based strategy for querycomputation. A pool of query candidates is precomputed, from which the bestcandidate according to some query quality criterion is selected to be shown tothe user. This often leads to the generation of many unnecessary querycandidates and thus to a high number of expensive calls to logical reasoningservices. We tackle this issue by an in-depth mathematical analysis of diversereal-valued active learning query selection measures in order to determinequalitative criteria that make a query favorable. These criteria are the key todevising efficient heuristic query search methods. The proposed methods enablefor the first time a completely reasoner-free query generation for interactiveKB debugging while at the same time guaranteeing optimality conditions, e.g.minimal cardinality or best understandability for the user, of the generatedquery that existing methods cannot realize.  Further, we study different relations between active learning measures. Theobtained picture gives a hint about which measures are more favorable in whichsituation or which measures always lead to the same outcomes, based on giventypes of queries.

Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging