Knowledge Source Discovery: An experience using Ontologies, WordNet and Artificial Neural Networks
This paper describes our continuing research on ontology-based knowledge source discovery on the Semantic Web. The research documented here is focused on discovering distributed knowledge sources from a user query using an Artificial Neural Network model. An experience using the Wordnet multilingual...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/104089 |
| Acceso en línea: | http://hdl.handle.net/11336/104089 |
| Access Level: | acceso abierto |
| Palabra clave: | ontology matching neural classifier wordnet https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | This paper describes our continuing research on ontology-based knowledge source discovery on the Semantic Web. The research documented here is focused on discovering distributed knowledge sources from a user query using an Artificial Neural Network model. An experience using the Wordnet multilingual database for the translation of the terms extracted from the user query and for their codification is presented here. Preliminary results provide us with the conviction that combining ANN with WordNet has clearly made the system much moreeficient. |
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