First Notes on Maximum Entropy Entailment for Quantified Implications

  • Francesco KriegelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10308)


Entropy is a measure for the uninformativeness or randomness of a data set, i.e., the higher the entropy is, the lower is the amount of information. In the field of propositional logic it has proven to constitute a suitable measure to be maximized when dealing with models of probabilistic propositional theories. More specifically, it was shown that the model of a probabilistic propositional theory with maximal entropy allows for the deduction of other formulae which are somehow expected by humans, i.e., allows for some kind of common sense reasoning.

In order to pull the technique of maximum entropy entailment to the field of Formal Concept Analysis, we define the notion of entropy of a formal context with respect to the frequency of its object intents, and then define maximum entropy entailment for quantified implication sets, i.e., for sets of partial implications where each implication has an assigned degree of confidence. Furthermore, then this entailment technique is utilized to define so-called maximum entropy implicational bases (ME-bases), and a first general example of such a ME-base is provided.


Maximum entropy Formal context Partial implication Formal Concept Analysis Implicational base Uncertain knowledge 



The author gratefully thanks the anonymous reviewers for their constructive hints and helpful remarks.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Theoretical Computer ScienceTechnische Universität DresdenDresdenGermany

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