An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases

Bosc, Patrick; Prade, Henri

doi:10.1007/978-1-4615-6245-0_10

Cited by 129 publications

(69 citation statements)

References 76 publications

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“…Fuzzy set theory [Zadeh 1965] has also been used as the basis for "soft" database systems [Bosc and Prade 1997]. Fagin [1998] and others have proposed algorithms that find the best few answers to a conjunctive query in this model.…”

Section: Related Workmentioning

confidence: 99%

Data integration using similarity joins and a word-based information representation language

Cohen

2000

ACM Trans. Inf. Syst.

159

108

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The integration of distributed, heterogeneous databases, such as those available on the World Wide Web, poses many problems. Here we consider the problem of integrating data from sources that lack common object identifiers. A solution to this problem is proposed for databases that contain informal, natural-language "names" for objects; most Web-based databases satisfy this requirement, since they usually present their information to the end-user through a veneer of text. We describe WHIRL, a "soft" database management system which supports "similarity joins," based on certain robust, general-purpose similarity metrics for text. This enables fragments of text (e.g., informal names of objects) to be used as keys. WHIRL includes textual objects as a built-in type, similarity reasoning as a built-in predicate, and answers every query with a list of answer substitutions that are ranked according to an overall score. Experiments show that WHIRL is much faster than naive inference methods, even for short queries, and efficient on typical queries to real-world databases with tens of thousands of tuples. Inferences made by WHIRL are also surprisingly accurate, equaling the accuracy of hand-coded normalization routines on one benchmark problem, and outperforming exact matching with a plausible global domain on a second.

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Section: Related Workmentioning

confidence: 99%

Data integration using similarity joins and a word-based information representation language

Cohen

2000

ACM Trans. Inf. Syst.

159

108

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“…Other applications of possibility theory can be found in fields such as data analysis [137,132,24], database querying [25], diagnosis [27,26], belief revision [18], argumentation [4,3], case-based reasoning [56,101], learning [120,121], and information merging [19] (taking advantage of the bipolar representation setting which distinguishes between positive information of the form ∆(φ) ≥ α and negative information expressing impossibility under the form N (φ) ≥ α ⇔ 1 − Π(¬φ) ≥ α [20]). …”

Section: Some Applicationsmentioning

confidence: 99%

Possibility Theory and Its Applications: Where Do We Stand?

Dubois

Prade

2015

Springer Handbook of Computational Intelligence

141

101

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This paper provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability and non-monotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and common-sense reasoning with exception-tainted knowledge in Artificial Intelligence. Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. Qualitative possibility theory has been axiomatically justified in a decision-theoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statistical reasoning with imprecise probabilities. As such it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information.

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“…This can be useful to further analyze if the selected reports deal with the same events, risk, requests and to validate their quality as it will be better described in Section 5. One may require that the results are retrieved with some tolerance of the selection conditions, which can be formulated by specifying linguistic terms defining soft constraints, and thus admitting degrees of satisfaction to the query (the relevance degree) [6]. These flexible queries are modeled by adopting the fuzzy database framework [4,5,12].…”

Section: Querying Facilitiesmentioning

confidence: 99%

Flexible Querying of Volunteered Geographic Information for Risk Management

Arcaini¹,

Bordogna²,

Sterlacchini³

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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The paper presents an approach to manage volunteered geographic information (VGI) to point out anomalous conditions of the environment to help administrators in charge of the governance and maintenance of the territory to plan mitigation and safeguard interventions. To this end they can formulate flexible queries on the VGI reports to analyze their contents. The novelty of the proposal is the search framework of VGI reports designed to support distinct needs, among which the assessment of VGI quality which is an important issue in such applications. Flexible queries are formulated and evaluated within a fuzzy database approach.

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An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases

Cited by 129 publications

References 76 publications

Data integration using similarity joins and a word-based information representation language

Data integration using similarity joins and a word-based information representation language

Possibility Theory and Its Applications: Where Do We Stand?

Flexible Querying of Volunteered Geographic Information for Risk Management

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