Abstract: The increase and diversification of information has created new user requirements. The problems of representation, processing, analysis and reasoning on the information, especially the uncertain information, are still an important research topic. We try to show in this paper how this issue is crucial. It is essential to propose new approaches to formally represent uncertain information to help machines understand and infer new knowledge. At first, we will start by defining the Bayesian networks, the way of it construction. Then will then focus on probabilistic ontologies and their relationship with Bayesian Networks. Then, we will show how we can integrate a Bayesian network into a probabilistic ontology citing the translation rul...
This paper presents our ongoing effort on developing a principled methodology for automatic ontology...
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Abstract: The increase and diversification of information has created new user requirements. The pro...
Today, ontologies are the standard for representing knowledge about concepts and relations among con...
We introduce the new probabilistic description logic (DL) BEL, which extends the light-weight DL EL ...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Abstract — This paper describes an ontology-driven model, which integrates Bayesian Networks (BN) in...
One of the major weaknesses of current research on the Semantic Web (SW) is the lack of proper means...
International audienceOntologies and probabilistic graphical models are considered within the most e...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Abstract:-Possibilistic logic and Bayesian networks have provided advantageous methodologies and tec...
We present a framework for probabilistic Information Processing on the Semantic Web that is capable ...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
International audienceProbabilistic Graphical Models (PGMs) are powerful tools for representing and ...
This paper presents our ongoing effort on developing a principled methodology for automatic ontology...
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Abstract: The increase and diversification of information has created new user requirements. The pro...
Today, ontologies are the standard for representing knowledge about concepts and relations among con...
We introduce the new probabilistic description logic (DL) BEL, which extends the light-weight DL EL ...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Abstract — This paper describes an ontology-driven model, which integrates Bayesian Networks (BN) in...
One of the major weaknesses of current research on the Semantic Web (SW) is the lack of proper means...
International audienceOntologies and probabilistic graphical models are considered within the most e...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Abstract:-Possibilistic logic and Bayesian networks have provided advantageous methodologies and tec...
We present a framework for probabilistic Information Processing on the Semantic Web that is capable ...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
International audienceProbabilistic Graphical Models (PGMs) are powerful tools for representing and ...
This paper presents our ongoing effort on developing a principled methodology for automatic ontology...
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...