Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of kno...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/196290 |
| Acceso en línea: | http://hdl.handle.net/11336/196290 |
| Access Level: | acceso abierto |
| Palabra clave: | ARTIFICIAL NEURAL NETWORK KNOWLEDGE-SOURCE DISCOVERY SEMANTIC WEB WORDNET https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | The fundamental principle of the Semantic Web is the creation and use of semantic annotations connected to formal descriptions, such as domain ontologies. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources, which are relevant to a user´s request. New eficient approaches for developing web intelligence and helping users to avoid irrelevant search results on the web have recently appeared. Artificial Neural Networks (ANN) being one of the most recent ones. However,there still remains a lot of work to be done in this area. This work makes a contribution to the field of knowledge-resource discovery and ontology matching techniques for the Semantic Web by presenting an approach which is based on an ANN classifier. Experimental results show that the ANN-based ontology matching model has provided satisfactory responses to the test cases. |
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