Fuzzy Description Logics – A Survey

Borgwardt, Stefan; Peñaloza, Rafael

doi:10.1007/978-3-319-67582-4_3

Cited by 22 publications

(5 citation statements)

References 83 publications

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“…Like their crisp counterparts, fuzzy OSF logic and fuzzy DLs (e.g., [40], [41]) share a few common aspects. For instance, fuzzy DL concepts are also interpreted as fuzzy subsets of a domain.…”

Section: Sort Intersectionmentioning

confidence: 99%

Similarity-Based Reasoning With Order-Sorted Feature Logic

Milanese,

Pasi

2024

IEEE Trans. Fuzzy Syst.

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Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning language based on sorts -symbols that denote concepts ordered in a subsumption relation -and features -symbols that denote functional attributes. Reasoning with OSF logic is based on the unification of OSF terms, recordlike structures that denote classes of objects and that are themselves ordered in a subsumption relation. OSF term unification aims to combine the constraints expressed by two terms in a consistent way, and it takes into account the subsumption relation between sort symbols, providing an efficient calculus of type subsumption. This paper presents an approach to define approximate reasoning with OSF logic by extending its language with a similarity relation on sorts. In order for the OSF term unification algorithm to take into account this similarity and its interaction with the subsumption relation, we propose to combine the two relations into a single fuzzy subsumption relation. The advantage is that the same unification rules of OSF logic can then be applied to this fuzzy setting. We conclude by discussing potential applications of OSF logic extended with a sort similarity relation.

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Section: Sort Intersectionmentioning

confidence: 99%

Similarity-Based Reasoning With Order-Sorted Feature Logic

Milanese,

Pasi

2024

IEEE Trans. Fuzzy Syst.

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“…The latter are notions which have no precise definition, or clear-cut separations between membership and non-membership (e.g., there is no specific height at which a person starts being tall, and under which they are not). These are studied in fuzzy and many-valued logics [7,8,10,11,32,35,67]. In these logics, it is possible to express a fact like Tall(a) : 0.8 which should be interpreted as saying that a is tall with a degree 0.8.…”

Section: Use Probabilitiesmentioning

confidence: 99%

Introduction to Probabilistic Ontologies

Peñaloza

2020

Reasoning Web. Declarative Artificial Intelligence

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There is no doubt about it; an accurate representation of a real knowledge domain must be able to capture uncertainty. As the best known formalism for handling uncertainty, probability theory is often called upon this task, giving rise to probabilistic ontologies. Unfortunately, things are not as simple as they might appear, and different choices made can deeply affect the semantics and computational properties of probabilistic ontology languages. In this tutorial, we explore the main design choices available, and the situations in which they may be meaningful or not. We then dive deeper into a specific family of probabilistic ontology languages which can express logical and probabilistic dependencies between axioms.

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“…Existem diversas extensões fuzzy de DL. Um bom ponto de partida para estudá-las é [Borgwardt and Peñaloza 2017]. Note os caracteres não-ASCII "ç" e "ã" acima.…”

Section: Outras Dlsunclassified

Semântica e Multimídia: Uma Introdução à Inteligência Artiﬁcial Simbólica Aplicada à Multimídia

Lima¹,

Costa²,

Ferreira³

2019

Minicursos Do XXV Simpósio Brasileiro De Sistemas Multimídia E Web

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In this chapter, we give an introduction to symbolic artificial intelligence (AI) from first principles and discuss its relation and application to multimedia. We begin by defining what symbolic AI is, what distinguishes it from non-symbolic approaches, such as machine learning, and how it can used in the construction of advanced multimedia applications. We then introduce description logic (DL) and use it to discuss symbolic representation and reasoning. DL is the logical underpinning of OWL, the most successful family of ontology languages. After discussing DL, we present OWL and related Semantic Web technologies, such as RDF and SPARQL. We conclude the chapter by discussing a hybrid model for multimedia representation, called Hyperknowledge. Throughout the chapter, we strive to make references to technologies and extensions specifically designed to solve the kinds of problems that arise in multimedia representation.XXV Simpósio Brasileiro de Sistemas Multimídia e Web: Minicursos XXV Simpósio Brasileiro de Sistemas Multimídia e Web: Minicursos

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Fuzzy Description Logics – A Survey

Cited by 22 publications

References 83 publications

Similarity-Based Reasoning With Order-Sorted Feature Logic

Similarity-Based Reasoning With Order-Sorted Feature Logic

Introduction to Probabilistic Ontologies

Semântica e Multimídia: Uma Introdução à Inteligência Artiﬁcial Simbólica Aplicada à Multimídia

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