A collaborative approach to ontology design
Communications of the ACM - Ontology: different ways of representing the same concept
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Uncertainty Handling and Quality Assesment in Data Mining
Uncertainty Handling and Quality Assesment in Data Mining
Context in problem solving: a survey
The Knowledge Engineering Review
A New Extraction Concept Based on Contextual Clustering
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Intelligent Ontology Learning based on Context: Answering Crucial Questions
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
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Ontology evaluation is vital for the development and the deployment of many applications like data annotation, retrieval information and semantic Web. In this paper, we expose a survey related to different aspects regarding ontology evaluation. Afterwards, we focus on the ontological concept evaluation task. We propose a new evaluation method based on a large collection of web documents and several context definitions deduced from it by applying a linguistic and a documentary analysis. Then based on these two context types, we define an algorithm which computes the credibility degree associated to each word cluster and to each context. Our algorithm titled Credibility Degree Computation and noted CDC helps the expert. It gives the possible word associations existing in these contexts, some semantic tags suggestions, deletes the noisy elements or moves them to their appropriate cluster. The CDC algorithm informs about the initial words of a given cluster and facilitates the evaluation task. Our evaluation method, which provides a qualitative and quantitative analysis, does not depend on a gold standard and it could be applied to any domain even if expert intervention is not available. Our experiments are conducted on French documents related to the tourism domain. The first obtained results show how our method assists and facilitates the evaluation task for the domain expert.