Local Ontologies Merging in Data Ponds

Abstract

Today, Ontologies have a major place in knowledge representation and modeling. They are used to formalize a domain knowledge and add a semantic layer to current systems and applications. Ontologies make it possible to explicitly represent the knowledge of a domain by means a formal language so that they can be manipulated automatically and shared easily. They are widely used in various fields of research such as Knowledge Representation (KR) and Data Integration (DI). However, the effectiveness to interoperate learning objects among various learning object repositories is often decreased because of using different ontological schemes for annotating learning objects into every learning object repository. Hence, semantic heterogeneity and structural differences between ontologies need to be resolved so as to generate common ontology to expedite learning object reusability. This paper focused on automated ontology mapping and merging concept. The study significance lies in an algorithmic approach for mapping attributes of learning objects/concepts and merging them based on mapped attributes; identifying suitable threshold value for mapping and merging.