TY - JOUR
T1 - Instance-based ontology matching for e-learning material using an associative pattern classifier
AU - Cerón-Figueroa, Sergio
AU - López-Yáñez, Itzamá
AU - Alhalabi, Wadee
AU - Camacho-Nieto, Oscar
AU - Villuendas-Rey, Yenny
AU - Aldape-Pérez, Mario
AU - Yáñez-Márquez, Cornelio
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
KW - Associative classifier
KW - E-learning
KW - Knowledge Society
KW - Ontology matching
KW - Pattern recognition
KW - Semantic Web
UR - http://www.scopus.com/inward/record.url?scp=85006944714&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2016.12.039
DO - 10.1016/j.chb.2016.12.039
M3 - Artículo
SN - 0747-5632
VL - 69
SP - 218
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -