Multi Criteria Decision Making in Fuzzy Description Logics: A First Step

Straccia, Umberto

doi:10.1007/978-3-642-04595-0_10

Cited by 17 publications

(13 citation statements)

References 10 publications

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“…These Several ideas combine fuzzy logic with MCDM: an interesting summary of this approach is presented by Kahraman [9]. Straccia [10] presents how to embed fuzzy description logic in MCDM with the perspective to use ontologies. But there are many others: for example, personnel selection in a human resources problem is tackled with a TOPSIS method and an ordered weighted average (in a study undertaken by Dursun and Karsak [11]); the best strategy for oil spill problems is determined by taking into account several experts' opinions with fuzzy evaluation methods (presented by Krohling and Rigo [12]).…”

Section: A Multi-criteria Decision Making and Fuzzinessmentioning

confidence: 99%

The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

Albertetti

Cotofrei

Grossrieder

et al. 2013

2013 European Intelligence and Security Informatics Conference

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Grouping events having similarities has always been interesting for analysts. Actually, when a label is put on top of a set of events to denote they share common properties, the automation and the capability to conduct reasoning with this set drastically increase. This is particularly true when considering criminal events for crime analysts; conjunction, interpretation and explanation can be key success factors to apprehend criminals. In this paper, we present the CriLiM methodology for investigating both serious and high-volume crime. Our artifact consists in implementing a tailored computerized crime linkage system, based on a fuzzy MCDM approach in order to combine spatio-temporal, behavioral, and forensic information. As a proof of concept, series in burglaries are examined from real data and compared to expert results.

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Section: A Multi-criteria Decision Making and Fuzzinessmentioning

confidence: 99%

The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

Albertetti

Cotofrei

Grossrieder

et al. 2013

2013 European Intelligence and Security Informatics Conference

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“…Actually, the connection between fuzzy ontologies and multicriteria decision making was already introduced and evaluated by Straccia (2009).…”

Section: Definition 3 (T-conorm) a T-conormmentioning

confidence: 99%

Decision making with a fuzzy ontology

2011

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Knowledge mobilisation is a transition from the prevailing knowledge management technology that has been widely used in industry for the last 20 years to a new methodology and some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation aims at coming to terms with some of the problems of knowledge management and at the same time to introduce new theory, new methods and new technology. More precisely, this paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making. We show that a number of soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy ontology. We demonstrate the knowledge mobilisation methods with the construction of a support system for finding the best available wine for a number of wine drinking occasions using a fuzzy wine ontology and fuzzy reasoning methods; the support system has been implemented for a Nokia N900 smart phone.

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“…An uncertainty-based approach which attempts to focus on multi-criteria decision making (MCDM) problems is [18]; it is mainly based on the application of general fuzzy logic to MCDM problems. Although the terms utility and preference are not explicitly used, it refers to preferences implicitly.…”

Section: Related Workmentioning

confidence: 99%

“…and knowledge representation, and on the other hand to apply methods from classical decision theory to improve decision support systems. In this regard there has been a growing interest over the last decade in the use of logics to model preferences, see [1,3,[14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Towards Decision Making via Expressive Probabilistic Ontologies

Acar

Thorne

Stuckenschmidt

2015

Algorithmic Decision Theory

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Abstract. We propose a framework for automated multi-attribute decision making, employing the probabilistic non-monotonic description logics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making situation, wherein background knowledge, available alternatives and weighted attributes are represented via probabilistic ontologies. It turns out that extending traditional utility theory with such description logics, enables us to model decision-making problems where probabilistic ignorance and default reasoning plays an important role. We provide several decision functions using the notions of expected utility and probability intervals, and study their properties.

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Multi Criteria Decision Making in Fuzzy Description Logics: A First Step

Cited by 17 publications

References 10 publications

The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach

Decision making with a fuzzy ontology

Towards Decision Making via Expressive Probabilistic Ontologies

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