Instance-Based Ontology Matching For Open and Distance Learning Materials

Abstract

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.

Keywords

• open and distance learning
• ontology matching problem
• e-learning
• pattern recognition
• associative classifier