Detecting Similar Areas of Knowledge Using Semantic and Data Mining Technologies
Searching for scientific publications online is an essential task for researchers working on a certain topic. However, the extremely large amount of scientific publications found in the web turns the process of finding a publication into a very difficult task whereas, locating peers interested in co...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Ecuador |
| Institución: | Universidad de Cuenca |
| Repositorio: | Repositorio Universidad de Cuenca |
| OAI Identifier: | oai:dspace.ucuenca.edu.ec:123456789/28948 |
| Acceso en línea: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004000331&doi=10.1016%2fj.entcs.2016.12.009&partnerID=40&md5=d83e9caafcab22c4d1a2781c8a8ddb4d http://dspace.ucuenca.edu.ec/handle/123456789/28948 |
| Access Level: | acceso abierto |
| Palabra clave: | Data Integration Data Mining Linked Data Query Languages Semantic Web |
| Sumario: | Searching for scientific publications online is an essential task for researchers working on a certain topic. However, the extremely large amount of scientific publications found in the web turns the process of finding a publication into a very difficult task whereas, locating peers interested in collaborating on a specific topic or reviewing literature is even more challenging. In this paper, we propose a novel architecture to join multiple bibliographic sources, with the aim of identifying common research areas and potential collaboration networks, through a combination of ontologies, vocabularies, and Linked Data technologies for enriching a base data model. Furthermore, we implement a prototype to provide a centralized repository with bibliographic sources and to find similar knowledge areas using data mining techniques in the domain of Ecuadorian researchers community. |
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