Ontología de patrón de comportamiento como controlador Web desde la nube para ahorro de energía en departamentos domotizados

La presente investigación tuvo como objetivo aplicar una Ontología de patrón de comportamiento como controlador Web desde la nube que permite anticiparse a las preferencias de los usuarios en el departamento único usado como modelo. Para ello, se desarrolló una Ontología de patrón de comportamiento como controlador Web, identificándose las características que tiene un departamento domotizado, se realizó un análisis de los términos y taxonomía de una Ontología con dispositivos de un departamento domotizado y patrón de comportamiento de personas habitantes en dicho departamento según la metodología Methontology y reutilizando conceptos de ontologías como la de DogOnt y DogPower, definiéndose relaciones binarias de una Ontología, sus atributos, axiomas y reglas de inferencia, e implementándose la Ontología desarrollada utilizando el software Protégé y el razonador Pellet. Luego de la aplicación de la Ontología y las mediciones efectuadas se concluye que la Ontología aplicada logra anticipar las preferencias de los habitantes en un departamento domotizado mediante reglas de inferencia y producen un ahorro del 17,43% de energía

Exploiting Ontology Recommendation Using Text Categorization Approach

Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies

The evaluation of ontologies: quality, reuse and social factors

Finding a “good” or the “right” ontology is a growing challenge in the ontology domain, where one of the main aims is to share and reuse existing semantics and knowledge. Before reusing an ontology, knowledge engineers not only have to find a set of appropriate ontologies for their search query, but they should also be able to evaluate those ontologies according to different internal and external criteria. Therefore, ontology evaluation is at the heart of ontology selection and has received a considerable amount of attention in the literature.Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges when it comes to evaluating and selecting ontologies for reuse. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics, e.g., content and structure of ontologies and ignore how they are used and evaluated by communities. This thesis aimed to investigate the notion of quality and reusability in the ontology domain and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. [Continues.