Foundations of statistical natural language processing
Foundations of statistical natural language processing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Methodologies, tools and languages for building ontologies: where is their meeting point?
Data & Knowledge Engineering
A model for matching semantic maps between languages (French/English, English/French)
Computational Linguistics
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Symbolic word clustering for medium-size corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Conceptual structuring through term variations
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
n-Gram Statistics for Natural Language Understanding and Text Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data transformation and query management in personal health sensor networks
Journal of Network and Computer Applications
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Many communities need to organize and structure data to improve their utilization and sharing. Much research has been focused on this problem. Many solutions are based on a Terminological and Ontological Resource (TOR) which represents the domain knowledge for a given application. However TORs are often designed without taking into account heterogeneous data from specific resources. For example, in the biomedical domain, these sources may be medical reports, bibliographical resources or biological data extracted from GOA, Gene Ontology or KEGG. This paper presents an integrated visual environment for knowledge engineering. It integrates heterogeneous data from domain databases. Relevant concepts and relations are thus extracted from data resources, using several analysis and treatment processes. The resulting ontology embryo is visualized through a user friendly adaptive interface displaying a knowledge map. The experiments and evaluations dealt with in this paper concern biological data.