Abstract Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one able stand out from others all aspects. An meta-matcher combines different alignment techniques explore various aspects of avoid performance being restricted some characteristics. The meta-matching process consists several stages execution, a...

Ontology is a means to help computer agents in the process of automatic knowledge extraction. In this way, ontology can be considered as a key factor in semantic web development. However, it only moves the problem of handling decentralized and distributed information over the Web to a higher level due to the fact that building of ontologies is not based on a common criteria. For example, simila...

Ontology mediation is enabled through interoperability of semantic data sources. It helps data sharing between heterogeneous knowledgebase and reuse by semantic applications. Ontology mediation includes operations such as, mapping, alignment, matching, merging and integration. This paper aims at discussing a new approach of ontology merging and matching using ontology abstract machine with an i...

Digital learning resource ontology is often based on different specification building. It is hard to find resources by linguistic ontology matching method. The existing structural matching method fails to solve the problem of calculation of structural similarity well. For the heterogeneity problem among learning resource ontology, an algorithm is presented based on subgraph approximate isomorph...

Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce ...