Autores
Yáñez Márquez Cornelio
Cerón Figueroa Sergio
Título Instance-based ontology matching for e-learning material using an associative pattern classifier
Tipo Revista
Sub-tipo JCR
Descripción Computers in Human Behavior
Resumen The present work describes a new model of pattern classification and its application to align instances from different ontologies, which are in turn related to e-learning educative content in a Knowledge Society context. In general, ontologies are the fundamental tool inherent to Semantic Web. In particular, the problem of ontology matching is modeled in this paper as a binary pattern classification problem. The original model presented here was validated through experiments, which were done on data taken from the OAEI (Ontology Alignment Evaluation Initiative) 2014 campaign, presented in the OWL (Web Ontology Language) format, as well as on data taken from two international repositories, ADRIADNE and MERLOT, in LOM (Learning Objects Metadata) format. The results obtained show a high precision measurement when compared against some of the best methods present in the state of the art.
Observaciones DOI /10.1016/j.chb.2016.12.039 Q1
Lugar Oxford
País Reino Unido
No. de páginas 218-225
Vol. / Cap. v. 69
Inicio 2017-04-01
Fin
ISBN/ISSN