Building large knowledge bases (KBs) is a fundamental task for automated reasoning and intelligent applications. Needing the interaction between domain and modeling knowledge, it is also error-prone. In fact, even well-maintained KBs are often found to lead to unwanted conclusions. We deal with two kinds of decisions associated with faulty KBs. First, which portions of the KB (and their conclusions) can still be trusted? Second, which is the correct way to repair the KB? Our solution to both problems is based on storing all the information about repairs in a compact data structure.
Penaloza, R. (2019). Making Decisions with Knowledge Base Repairs. In Modeling Decisions for Artificial Intelligence (pp.259-271). Springer Verlag [10.1007/978-3-030-26773-5_23].
Making Decisions with Knowledge Base Repairs
Penaloza R.
2019
Abstract
Building large knowledge bases (KBs) is a fundamental task for automated reasoning and intelligent applications. Needing the interaction between domain and modeling knowledge, it is also error-prone. In fact, even well-maintained KBs are often found to lead to unwanted conclusions. We deal with two kinds of decisions associated with faulty KBs. First, which portions of the KB (and their conclusions) can still be trusted? Second, which is the correct way to repair the KB? Our solution to both problems is based on storing all the information about repairs in a compact data structure.
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