Reasoning in inconsistent knowledge bases

1995 ◽  
Vol 7 (1) ◽  
pp. 177-189 ◽  
Author(s):  
J. Grant ◽  
V.S. Subrahmanian
Keyword(s):  
Knowledge Bases ◽  
Inconsistent Knowledge

scholarly journals Measuring Inconsistency in Probabilistic Knowledge Bases

2017 ◽  
Author(s):  
Glauber De Bona
Keyword(s):  
Knowledge Bases ◽  
Probabilistic Logic ◽  
Gambling Behaviour ◽  
Dutch Books ◽  
Probabilistic Knowledge ◽  
Probabilistic Beliefs ◽  
Inconsistent Knowledge ◽  
Rationality Postulates

In AI, inconsistency measures have been proposed as a way to manage inconsistent knowledge bases. This work investigates inconsistency measuring in probabilistic logic. We show that previously existing rationality postulates for inconsistency measures in probabilistic knowledge bases are themselves incompatible and introduce a new way of localising inconsistency to reconcile these postulates. We then show the equivalence between distance-based inconsistency measures, from the AI community, and incoherence measures, from philosophy, that are based on the disadvantageous gambling behaviour entailed by incoherent probabilistic beliefs (via Dutch books). This provides a meaningful interpretation to the former and efficient methods to compute the latter.

scholarly journals A Dialogue System for Coherent Reasoning with Inconsistent Knowledge Bases

2015 ◽  
Vol 03 (08) ◽  
pp. 11-19 ◽  
Author(s):  
Silvio do Lago Pereira ◽  
Luiz Felipe Zarco dos Santos ◽  
Lucio Nunes de Lira
Keyword(s):  
Knowledge Bases ◽  
Dialogue System ◽  
Inconsistent Knowledge

scholarly journals Explanations for Inconsistency-Tolerant Query Answering under Existential Rules

2020 ◽  
Vol 34 (03) ◽  
pp. 2909-2916
Author(s):  
Thomas Lukasiewicz ◽  
Enrico Malizia ◽  
Cristian Molinaro
Keyword(s):  
Knowledge Base ◽  
Complexity Analysis ◽  
Knowledge Bases ◽  
Query Answering ◽  
Complexity Measures ◽  
Rule Languages ◽  
Wide Range ◽  
Inconsistent Knowledge

Querying inconsistent knowledge bases is a problem that has attracted a great deal of interest over the last decades. While several semantics of query answering have been proposed, and their complexity is rather well-understood, little attention has been paid to the problem of explaining query answers. Explainability has recently become a prominent problem in different areas of AI. In particular, explaining query answers allows users to understand not only what is entailed by an inconsistent knowledge base, but also why. In this paper, we address the problem of explaining query answers for existential rules under three popular inconsistency-tolerant semantics, namely, the ABox repair, the intersection of repairs, and the intersection of closed repairs semantics. We provide a thorough complexity analysis for a wide range of existential rule languages and for different complexity measures.

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