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Rough Forgetting

Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form [Formula: see text], where [Formula: see text] is a set of relations in the language signature used to specify th...

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Detalles Bibliográficos
Autores principales: Doherty, Patrick, Szałas, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338204/
http://dx.doi.org/10.1007/978-3-030-52705-1_1
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author Doherty, Patrick
Szałas, Andrzej
author_facet Doherty, Patrick
Szałas, Andrzej
author_sort Doherty, Patrick
collection PubMed
description Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form [Formula: see text], where [Formula: see text] is a set of relations in the language signature used to specify the KB. The result of this operation is a new KB where the relations in [Formula: see text] are removed from the KB in a principled manner resulting in a more efficient representation of the KB for different purposes. The forgetting operator is also reflected semantically in terms of the relation between the original models of the KB and the models for the revised KB after forgetting. In this paper, we first develop a rough reasoning framework where our KB’s consist of rough formulas with a semantics based on a generalization of Kleene algebras. Using intuitions from the classical case, we then define a forgetting operator that can be applied to rough KBs removing rough relations. A constructive basis for generating a new KB as the result of applying the forgetting operator to a rough KB is specified using second-order quantifier elimination techniques. We show the application of this technique with some practical examples.
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spelling pubmed-73382042020-07-07 Rough Forgetting Doherty, Patrick Szałas, Andrzej Rough Sets Article Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form [Formula: see text], where [Formula: see text] is a set of relations in the language signature used to specify the KB. The result of this operation is a new KB where the relations in [Formula: see text] are removed from the KB in a principled manner resulting in a more efficient representation of the KB for different purposes. The forgetting operator is also reflected semantically in terms of the relation between the original models of the KB and the models for the revised KB after forgetting. In this paper, we first develop a rough reasoning framework where our KB’s consist of rough formulas with a semantics based on a generalization of Kleene algebras. Using intuitions from the classical case, we then define a forgetting operator that can be applied to rough KBs removing rough relations. A constructive basis for generating a new KB as the result of applying the forgetting operator to a rough KB is specified using second-order quantifier elimination techniques. We show the application of this technique with some practical examples. 2020-06-10 /pmc/articles/PMC7338204/ http://dx.doi.org/10.1007/978-3-030-52705-1_1 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Doherty, Patrick
Szałas, Andrzej
Rough Forgetting
title Rough Forgetting
title_full Rough Forgetting
title_fullStr Rough Forgetting
title_full_unstemmed Rough Forgetting
title_short Rough Forgetting
title_sort rough forgetting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338204/
http://dx.doi.org/10.1007/978-3-030-52705-1_1
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