Handling Uncertain Information: A Review of Numeric and Non-numeric Methods

Bhatnagar, Raj; Kanal, Laveen N.

doi:10.1016/b978-0-444-70058-2.50006-1

Cited by 58 publications

(25 citation statements)

References 10 publications

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“…Regarding uncertainty in trust management, uncertainty logics provide various methods for representing and updating uncertainty/belief [3]. Jøsang [12] proposed subjective logic to represent uncertain trust values with an opinion triangle in which an opinion is represented as a triple (b, d, u) where b, d, u denote the degrees of belief, disbelief, and uncertainty respectively, and the sum of them equals to 1.…”

Section: Related Researchmentioning

confidence: 99%

Uncertainty in Knowledge Provenance

Huang

Fox

2004

Lecture Notes in Computer Science

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Abstract. Knowledge Provenance is an approach to determining the origin and validity of knowledge/information on the web by means of modeling and maintaining information sources and dependencies, as well as trust structures. This paper constructs an uncertainty-oriented Knowledge Provenance model to address the provenance problem with uncertain truth values and uncertain trust relationships by using information theory and probability theory. This proposed model could be used for both people and web applications to determine the validity of web information in a world where information is uncertain.

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Section: Related Researchmentioning

confidence: 99%

Uncertainty in Knowledge Provenance

Huang

Fox

2004

Lecture Notes in Computer Science

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“…It has been observed that "very little has been done to perform reasoning or inferencing with information represented in terms of belief functions" [4]. Hence, using a domain ontology, which already provides a reasoning strategy in its structure to integrate this information seems to be a promising suggestion.…”

Section: Dempster-shafer Theorymentioning

confidence: 99%

“…For a more detailed presentation of Dempster's Rule, we refer the reader to [18], chapter 3, §1. A major advantage of the Dempster-Shafer theory is its explicit representation of ignorance [4] as it makes a clear distinction between the lack of knowledge and the assignment of degrees of belief to the negation of a proposition. Hence, there is no need for the belief of a proposition and the belief of its negation to sum up to 1 because remaining propositions will not be affected without explicit assignments.…”

Section: Dempster-shafer Theorymentioning

confidence: 99%

Integrating Uncertain Knowledge in a Domain Ontology for Room Concept Classifications

Hois

Schill

Bateman

2007

Research and Development in Intelligent Systems XXIII

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Ontologies provide a representation of precise knowledge about concepts, their attributes and relations. The Dempster-Shafer theory provides a representation of epistemic plausibilities. In AI both representations are typically developed separately on purpose, which is appropriate unless their combination is required. Real world applications, however, sometimes require a combination of both.In this paper we will present such a combination of ontological and uncertain knowledge. Our approach arises from the need of a room classification system for representing room concepts (in the sense of classifying names that are cognitively assigned to rooms, such as 'kitchen', 'laboratory', 'office') that can be derived from objects occurring in the rooms. These room concepts can only be determined with a certain degree of belief, not so much depending on the system's quality as depending on ambiguities in the cognitive assignment of room concepts. Hence, uncertainty about concepts that exist in reality also needs to be represented in the application.

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“…Interval structure provides a common framework that unifies the notions of lower/upper incidence sets [Bundy, 1985] and rough sets [Pawlak, 1982[Pawlak, , 1984. The non-numeric approach is also useful in symbolic computation, machine learning, classification and clustering [Bhatnagar and Kanal, 1986;Jean-Louis, 1991;Luzeaux, 1991;Orlowska, 1985;Pawlak, 1984].…”

Section: Introductionmentioning

confidence: 99%

“…These mechanisms can be broadly divided into two categories: the numeric and non-numeric (or the qualitative and quantitative) approaches, which capture different but complementary aspects of uncertain knowledge [Bhatnagar and Kanal, 1986]. Typical examples of the numeric approach are probabilistic logic [Nilsson, 1986;Rescher, 1969], fuzzy logic [Dubois and Prade, 1988], belief measures [Nguyen, 1978;Shafer, 1976Shafer, , 1987Smets, 1988] and the calculus of certainty factors [Shortliffe, 1976].…”

Section: Introductionmentioning

confidence: 99%

A Non-Numeric Approach to Uncertain Reasoning

Yao

Wong

Wang

1995

International Journal of General Systems

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This paper presents a non-numeric approach to uncertain reasoning by extending the incidence calculus. In parallel to the well known fuzzy, belief/plausibility, probability, and necessity/possibility measures, the corresponding classes of non-numeric functions are examined. A method of constructing non-numeric functions is discussed using the notion of compatibility relations. Non-numeric functions are used to interpret uncertain reasoning by providing possible-worlds semantics for both qualitative and quantitative methods.INDEX TERMS: Belief/plausibility, fuzzy measure, incidence calculus, interval structure, necessity/possibility, probability, non-numeric functions, rough sets, uncertain reasoning.

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Handling Uncertain Information: A Review of Numeric and Non-numeric Methods

Cited by 58 publications

References 10 publications

Uncertainty in Knowledge Provenance

Uncertainty in Knowledge Provenance

Integrating Uncertain Knowledge in a Domain Ontology for Room Concept Classifications

A Non-Numeric Approach to Uncertain Reasoning

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