Abstract Manual ontology development and evolution are complex and time-consuming tasks, even when textual documents are used as knowledge sources in addition to human expertise or existing ontologies. Processing natural language in text produces huge amounts of linguistic data that need to be filtered out and structured. To support both of these tasks, we have developed DYNAMO-MAS, an interactive tool based on an adaptive multi-agent system (adaptive MAS or AMAS) that builds and evolves ontologies from text. DYNAMO-MAS is a partner system to build ontologies; the ontologist interacts with the system to validate or modify its outputs. This paper presents the architecture of DYNAMO-MAS, its operating principles and its evaluation on three case studies.
Sellami et al. in REF develop DYNAMO-MAS, an interactive tool based on an adaptive multi-agent system (AMAS).
DYNAMO-MAS: a Multi-Agent System for Ontology Evolution from Text
Journal on Data Semantics
Journal on Data Semantics
Computer Science