TY - JOUR
T1 - Instance-based ontology matching for open and distance learning materials
AU - Cerón-Figueroa, Sergio
AU - López-Yáñez, Itzamá
AU - Villuendas-Rey, Yenny
AU - Camacho-Nieto, Oscar
AU - Aldape-Pérez, Mario
AU - Yáñez-Márquez, Cornelio
PY - 2017
Y1 - 2017
N2 - The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies -both in the Learning Objects Metadata (LOM) format- into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.
AB - The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies -both in the Learning Objects Metadata (LOM) format- into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.
KW - Associative classifier
KW - E-learning
KW - Ontology matching problem
KW - Open and distance learning
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85014079601&partnerID=8YFLogxK
U2 - 10.19173/irrodl.v18i1.2681
DO - 10.19173/irrodl.v18i1.2681
M3 - Artículo
SN - 1492-3831
VL - 18
SP - 177
EP - 195
JO - International Review of Research in Open and Distance Learning
JF - International Review of Research in Open and Distance Learning
IS - 1
ER -