Abstract
Manual ontology engineering and maintenance is a difficult task that requires significant effort from the ontologist to identify and structure domain knowledge. Automatic ontology learning makes this task easier, especially through the use of text and natural language processing tools. In this paper, we present DYNAMO, a tool based on an Adaptive Multi-Agent System (AMAS), which aims at helping ontologists during ontology building and evolution (co-construction process). DYNAMO is based on terms and lexical relations that have been extracted from text. DYNAMO provides an AMAS based module to support ontology co-construction. The ontologist interacts with the tool by modifying the ontology. Then the AMAS adapts to these changes and proposes new evolutions to improve the ontology. A first experiment of ontology building shows promising results, and helps us to identify key issues in the agent behaviour that should be solved so that the DYNAMO performs better.
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Sellami, Z., Camps, V., Aussenac-Gilles, N., Rougemaille, S. (2011). Ontology Co-construction with an Adaptive Multi-Agent System: Principles and Case-Study. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowlege Engineering and Knowledge Management. IC3K 2009. Communications in Computer and Information Science, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19032-2_18
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DOI: https://doi.org/10.1007/978-3-642-19032-2_18
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