Learn More
Learning Non-Taxonomic Relationships is a sub-field of Ontology learning that aims at automating the extraction of these relationships from text. This article proposes PARNT, a novel approach that supports ontology engineers in extracting these elements from corpora of plain English. PARNT is parametrized, extensible and uses original solutions that help to(More)
Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology.(More)
Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for Learning Non-Taxonomic(More)
A new controller configuration for Robust Model Reference Control is presented in this paper. The structure is based on a right coprime factorization of the plant and makes use of an observer-based feedback control scheme combined with a prefilter controller. It is designed first to guarantee stability robustness and some levels of performance in terms of(More)
Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. This article discusses the problem of Learning Non-Taxonomic Relationships of ontologies and proposes a generic process for approaching it. Some techniques representing the state of the art of this field are(More)
The 2-DOF Observer-Controller configuration is based on a right coprime factorization of the plant and makes use of an observer-based feedback control scheme combined with a prefilter controller. A two-step design procedure is accomplished: first, we can design to guarantee stability robustness and some levels of performance in terms of disturbance(More)