This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice’s query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user’s Network or an object in the user’s Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as th
This paper proposes a unified approach to learning from constraints, which integrates the ability of...
How Bayesian inference might be used as the basis of a system for learning and representing the mean...
Developing the expert system (ES) using conventional programming languages is very tedious task. The...
Fuzzy knowledge representation, learning and optimization with Bayesian analysis in fuzzy semantic ...
[[abstract]]In real applications, data provided to a learning system usually contain linguistic info...
Abstract. In real applications, data provided to a learning system usually contain linguistic inform...
Knowledge representation is an emerging field of research in Artificial Intelligence, Big data analy...
This research aims at promoting the usage of an online children's dictionary within a context of rea...
This paper will report on one of the central objectives of a project in computational semantics whic...
Processing fuzzy information in semantic web applications / S. Klöckner, K. Turowski, U. Weng. - In:...
The Bayesian network (BN) structure learning from the observational data has been proved to be a NP-...
In this paper, we propose a method for learning ontologies used to model a domain in the field of in...
This paper proposes a neural network for building and optimizing fuzzy models. The network can be re...
International audienceFuzzy ontologies allow the modeling of real world environments using fuzzy set...
A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy de...
This paper proposes a unified approach to learning from constraints, which integrates the ability of...
How Bayesian inference might be used as the basis of a system for learning and representing the mean...
Developing the expert system (ES) using conventional programming languages is very tedious task. The...
Fuzzy knowledge representation, learning and optimization with Bayesian analysis in fuzzy semantic ...
[[abstract]]In real applications, data provided to a learning system usually contain linguistic info...
Abstract. In real applications, data provided to a learning system usually contain linguistic inform...
Knowledge representation is an emerging field of research in Artificial Intelligence, Big data analy...
This research aims at promoting the usage of an online children's dictionary within a context of rea...
This paper will report on one of the central objectives of a project in computational semantics whic...
Processing fuzzy information in semantic web applications / S. Klöckner, K. Turowski, U. Weng. - In:...
The Bayesian network (BN) structure learning from the observational data has been proved to be a NP-...
In this paper, we propose a method for learning ontologies used to model a domain in the field of in...
This paper proposes a neural network for building and optimizing fuzzy models. The network can be re...
International audienceFuzzy ontologies allow the modeling of real world environments using fuzzy set...
A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy de...
This paper proposes a unified approach to learning from constraints, which integrates the ability of...
How Bayesian inference might be used as the basis of a system for learning and representing the mean...
Developing the expert system (ES) using conventional programming languages is very tedious task. The...
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