A New Kind of Implication to Reason with Unknown Information

Pérez-Gámez, Francisco; Enciso, Manuel; Mora, Ángel

doi:10.1007/978-3-030-77867-5_5

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Two promising trends are to take full advantage of negative information (i.e. the information contained in the negative existing context K), as in [33] or [27], and to assign weights to attributes, as in [3].…”

Section: Discussionmentioning

confidence: 99%

Mining for Unknown Unknowns

Sinclair‐Desgagné

2023

Electron. Proc. Theor. Comput. Sci.

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Unknown unknowns are future relevant contingencies that lack an ex ante description. While there are numerous retrospective accounts showing that significant gains or losses might have been achieved or avoided had such contingencies been previously uncovered, getting hold of unknown unknowns still remains elusive, both in practice and conceptually. Using Formal Concept Analysis (FCA) -a subfield of lattice theory which is increasingly applied for mining and organizing data -this paper introduces a simple framework to systematically think out of the box and direct the search for unknown unknowns.There are only two kinds of campaign plans, good ones and bad ones.The good ones almost always fail through unforeseen circumstances that often make the bad ones succeed.-Napoleon Bonaparte -* I am grateful to the participants at several seminars for constructive questions and remarks. I also owe special thanks to

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Section: Discussionmentioning

confidence: 99%

Mining for Unknown Unknowns

Sinclair‐Desgagné

2023

Electron. Proc. Theor. Comput. Sci.

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Simplification logic for the management of unknown information

Pérez-Gámez

Enciso

Mora

2023

Information Sciences

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Simplifying Implications with Positive and Negative Attributes: A Logic-Based Approach

et al. 2022

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Concepts and implications are two facets of the knowledge contained within a binary relation between objects and attributes. Simplification logic (SL) has proved to be valuable for the study of attribute implications in a concept lattice, a topic of interest in the more general framework of formal concept analysis (FCA). Specifically, SL has become the kernel of automated methods to remove redundancy or obtain different types of bases of implications. Although originally FCA used only the positive information contained in the dataset, negative information (explicitly stating that an attribute does not hold) has been proposed by several authors, but without an adequate set of equivalence-preserving rules for simplification. In this work, we propose a mixed simplification logic and a method to automatically remove redundancy in implications, which will serve as a foundational standpoint for the automated reasoning methods for this extended framework.

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A New Kind of Implication to Reason with Unknown Information

Cited by 3 publications

References 20 publications

Mining for Unknown Unknowns

Mining for Unknown Unknowns

Simplification logic for the management of unknown information

Simplifying Implications with Positive and Negative Attributes: A Logic-Based Approach

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