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Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².


Papers
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Journal ArticleDOI
TL;DR: Fuzzy OWL is created, a fuzzy extension to OWL that can capture imprecise and vague knowledge, and the reasoning platform, fuzzy reasoning engine (FiRE), lets FuzzY OWL capture and reason about such knowledge.
Abstract: The semantic Web must handle information from applications that have special knowledge representation needs and that face uncertain, imprecise knowledge. More precisely, some applications deal with random information and events, others deal with imprecise and fuzzy knowledge, and still others deal with missing or distorted information - resulting in uncertainty. To deal with uncertainty in the semantic Web and its applications, many researchers have proposed extending OWL and the description logic (DL) formalisms with special mathematical frameworks. Researchers have proposed probabilistic, possibilistic, and fuzzy extensions, among others. Researchers have studied fuzzy extensions most extensively, providing impressive results on semantics, reasoning algorithms, and implementations. Building on these results, we've created a fuzzy extension to OWL called Fuzzy OWL. Fuzzy OWL can capture imprecise and vague knowledge. Moreover, our reasoning platform, fuzzy reasoning engine (FiRE), lets Fuzzy OWL capture and reason about such knowledge

159 citations

Book ChapterDOI
29 Jul 1994
TL;DR: The language ACCP is presented, which is a probabilistic extension of terminological logics and aims at closing the gap between the two areas of research.
Abstract: On the one hand, classical terminological knowledge representation excludes the possibility of handling uncertain concept descriptions involving, e.g., "usually true" concept properties, generalized quantifiers, or exceptions. On the other hand, purely numerical approaches for handling uncertainty in general axe unable to consider terminological knowledge. This paper presents the language ACCP which is a probabilistic extension of terminological logics and aims at closing the gap between the two areas of research. We present the formal semantics underlying the language ACCP and introduce the probabilistic formalism that is based on classes of probabilities and is realized by means of probabilistic constraints. Besides infering implicitly existent probabilistic relationships, the constraints guarantee terminological and probabilistic consistency. Altogether, the new language ACCP applies to domains where both term descriptions and uncertainty have to be handled.

158 citations

01 Jan 1992
TL;DR: A unified framework is developed through an analysis of various types, aspects and roles of knowledge relevant for the kind of systems described above, which aims to provide an environment for discussion of different approaches to knowledge intensive problem solving and learning.
Abstract: The problem addressed in this research is that of developing a method which integrates problem solving with learning from experience within an extensive model of different knowledge types. A unified framework is developed through an analysis of various types, aspects and roles of knowledge relevant for the kind of systems described above. The framework contains a knowledge representation platform and a generic model of problem solving. It further specifies a general reasoning approach that combines reasoning within a deep model with reasoning from heuristic rules and past cases. Finally, it provides a model of learning methods that retain concrete problem solving cases in a way that makes them useful for solving similar problems later. The framework emphasizes knowledge-intensive case-based reasoning and learning as the major paradigm. A comprehensive and thorough knowledge model is the basis for generation of goal related explanations that support the reasoning and learning processes. Reasoning from heuristic rules or from 'scratch' within the deeper model is regarded partly as supporting methods to the case-based reasoning, partly as methods to 'fall back on' if the case-based method fails. The purpose of the framework is to provide an environment for discussion of different approaches to knowledge intensive problem solving and learning. Four systems focus on different methodological issues of knowledge intensive problem solving and learning. Each system represents interesting solutions to subproblems, but none of them provide a scope that is broad enough to represent the type of method requested for developing and maintaining complex applications in a practical, real world setting. CREEK specifies a structural and functional architecture based on an expressive, frame-based knowledge representation language, and an explicit model of control knowledge. It has a reasoning strategy which first attempts case-based reasoning, then rule-based reasoning, and, finally, model-based reasoning. The system interacts with the user during both problem solving and learning, e.g. by asking for confirmation or rejection of unexplained facts. The knowledge representation system, including an explicit model of basic representational constructs and basic inference methods, has been implemented. Otherwise, CREEK is an architectural specification--a system design. Its main characteristics are demonstrated by an example from the domain of diagnosis and treatment of oil well drilling fluid (mud). (Abstract shortened with permission of author.)

158 citations

Journal ArticleDOI
TL;DR: This article reviews existing similarity measures in geometric, feature, network, alignment and transformational models, and evaluates the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data.
Abstract: Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.

158 citations

Journal ArticleDOI
TL;DR: Detailed examples illustrate the various benefits of adopting the AH as a knowledge representation framework, namely: providing sufficient representations to allow reasoning about unanticipated fault and control situations, allowing the use of reasoning mechanisms that are independent of domain information, and having psychological relevance.
Abstract: The abstraction hierarchy (AH) is a multileveled representation framework, consisting of physical and functional system models, which has been proposed as a useful framework for developing representations of complex work environments Despite the fact that the AH is well known and widely cited in the cognitive engineering community, there are surprisingly few examples of its application Accordingly, the intent of this paper is to provide a concrete example of how the AH can be applied as a knowledge representation framework A formal instantiation of the AH as the basis for a computer program is presented in the context of a thermal-hydraulic process This model of the system is complemented by a relatively simple reasoning mechanism which is independent of the information contained in the knowledge representation This reasoning mechanism uses the AH model, along with qualitative user input about system states, to generate reasoning trajectories for different types of events and problems Simulation outputs showing how the AH model can provide an effective basis for reasoning under different classes of situations, including challenging faults of various types, are presented These detailed examples illustrate the various benefits of adopting the AH as a knowledge representation framework, namely: providing sufficient representations to allow reasoning about unanticipated fault and control situations, allowing the use of reasoning mechanisms that are independent of domain information, and having psychological relevance

158 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202378
2022192
2021390
2020528
2019566
2018509