Some expert systems need common sense
Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer
Context and structure in automated full-text information access
Context and structure in automated full-text information access
Concept-based knowledge discovery in texts extracted from the Web
ACM SIGKDD Explorations Newsletter
Accordion summarization for end-game browsing on PDAs and cellular phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Automatic Summarization of Japanese Sentences and its Application to a WWW KWIC Index
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
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
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Relation extraction and validation algorithm
ICDCIT'07 Proceedings of the 4th international conference on Distributed computing and internet technology
A model-driven approach of ontological components for on- line semantic web information retrieval
Journal of Web Engineering
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Ontologies provide a common layer which plays a major role in supporting information exchange and sharing. In this paper, we focus on the ontological concept extraction process from HTML documents. In order to improve this process, we propose an unsupervised hierarchical clustering algorithm namely "Contextual Ontological Concept Extraction" (COCE) which is an incremental use of the partitioning algorithm Kmeans and is guided by a structural context. Our context exploits the html structure and the location of words to select the semantically closer cooccurrents for each word and to improve the words weighting. Guided by this context definition, we perform an incremental clustering that refines the context of each word clusters to obtain semantically extracted concepts. The COCE algorithm offers the choice between either an automatic execution or a user's interaction. We experiment our algorithm on HTML documents related to the tourism domain. Our results show how the execution of our context-based algorithm which implements an incremental process and a successive refinement of clusters improves their conceptual quality and the relevance of the extracted ontological concepts.