Foundations of statistical natural language processing
Foundations of statistical natural language processing
Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Living Design for Open Computational Systems
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Reconciling ontological differences by assistant agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Text analysis for ontology and terminology engineering
Applied Ontology
Evolva: A Comprehensive Approach to Ontology Evolution
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Supporting small teams in cooperatively building application domain models
Expert Systems with Applications: An International Journal
Journal of Web Engineering
Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
Journal of Computational Neuroscience
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Ontologies building from text is still a time-consuming task which justifies the growth of Ontology Learning. Our system named Dynamo is designed along this domain but following an original approach based on an adaptive multi-agent architecture. In this paper we present a distributed hierarchical clustering algorithm, core of our approach. It is evaluated and compared to a more conventional centralized algorithm. We also present how it has been improved using a multi-criteria approach. With those results in mind, we discuss the limits of our system and add as perspectives the modifications required to reach a complete ontology building solution.