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We propose a mechanism, based on Kinght\u2019s [14] study of inconsistency, for revising an inconsistent set of premisses to a minimally uncertain, probabilistically consistent one. We will then generalise the probabilistic entailment relation introduced in [15] for propositional languages to the first order case to draw logical inference from a probabilistic set of premisses. We will show how this combination can allow us to limit the effect of uncertainty introduced by inconsistent premisses to only the reasoning on the part of the premise set that is relevant to the inconsistency.<\/jats:p>","DOI":"10.1017\/s1755020322000235","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T08:45:11Z","timestamp":1657183511000},"page":"351-368","update-policy":"http:\/\/dx.doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["PROBABILISTIC ENTAILMENT ON FIRST ORDER LANGUAGES AND REASONING WITH INCONSISTENCIES"],"prefix":"10.1017","volume":"16","author":[{"given":"SOROUSH","family":"RAFIEE RAD","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"S1755020322000235_r9","unstructured":"[9] De Bona, G. , Finger, M. , Ribeiro, M. , Santos, Y. , & Wassermann, R. (2016). Consolidating probabilistic knowledge bases via belief contraction. 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