Learning possibilistic networks from data: a survey

Haddad, Maroua; Leray, Philippe; Amor, Nahla Ben

doi:10.2991/ifsa-eusflat-15.2015.30

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…For possibilistic networks, parameter learning from data consists basically in deriving conditional local possibility distributions from data. There are two main approaches for learning the parameters [Haddad et al, 2015]: i) Transformation-based approach: It first consists in learning probability distributions from data then transforming them into possibilistic ones using probabilitypossibility transformations [Benferhat et al, 2015a]. ii) Possibilistic-based approach: Such approaches stem from some quantitative interpretations of possibility distributions.…”

Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%

Belief Graphical Models for Uncertainty Representation and Reasoning

Benferhat

Leray

Tabia

2020

A Guided Tour of Artificial Intelligence Research

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Many real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.

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Section: Fig 8 Example Of a Possibilistic Networkmentioning

confidence: 99%

Belief Graphical Models for Uncertainty Representation and Reasoning

Benferhat

Leray

Tabia

2020

A Guided Tour of Artificial Intelligence Research

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“…While our approach is based on a probabilistic model, possibility theory [61,62,63,64] provides an orthogonal approach for matching computation. While the choice of the underlying model depends on the application domain, our motivation for a probabilistic model is threefold.…”

Section: Related Workmentioning

confidence: 99%

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Hùng

Weidlich

Tâm

et al. 2019

Information Systems

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Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.

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Possibilistic Graphical Models for Uncertainty Modeling

Tabia

2016

Lecture Notes in Computer Science

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Learning possibilistic networks from data: a survey

Cited by 4 publications

References 29 publications

Belief Graphical Models for Uncertainty Representation and Reasoning

Belief Graphical Models for Uncertainty Representation and Reasoning

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Possibilistic Graphical Models for Uncertainty Modeling

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