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This article proposes a process for automatic population of ontologies from text that applies natural language processing and information extraction techniques to acquire and classify ontology instances. The work is part of HERMES, an FCT/CAPES research project looking for techniques and tools for automating the process of ontology learning and population. Two experiments using a legal and a tourism corpora were conducted in order to evaluate it. The results indicate that our approach can extract and classify instances with high effectiveness with the additional advantage of domain independence.

Knowledge systems are a suitable computational approach to solve complex problems and to provide decision support. Ontologies are an approach for knowledge representation and Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Thus, automatic or semi-automatic approaches are needed. This paper discusses the problem of Automatic Ontology Population and proposes a generic process specifying its phases and what kind of techniques can be used to perform the activities of each phase. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the AOP process with their advantages and limitations. This work is part of HERMES, a Brazil/Portugal research cooperation project looking for techniques and tools for automating the process of ontology learning and population.

Because of its facilities for the generalization and specialization of concepts and the unambiguous terminology they provide, ontologies are being used for the representation of reusable software artifacts. This work describes GRAMO, an ontology-based technique for the specification of domain and user models in Multi-Agent Domain Engineering. ONTODUM, an ontology-based tool supporting GRAMO is also introduced. ONTODUM represents the knowledge of GRAMO. Some case studies developed to evaluate GRAMO are also briefly described.

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