Semi-Automatic Merging of Ontologies using Protégé

Abstract

Ontologies play a crucial role in bringing the Semantic Web vision to its full potential. Ontologies are developed at different levels of abstraction and by different people for various purposes. Knowledge represented by the ontologies is scattered because of existence of many ontologies representing the same concepts, therefore it becomes difficult to analyse, study and use the knowledge spread across many ontologies if they are studied individually. Knowledge represented by ontologies can be combined into a single ontology which is shown in this paper. Ontologies can be merged to combine the knowledge from different ontologies. In this paper we have shown the merger of two ontologies first ontology is university second ontology is the Student profile ontology containing details of Student which were developed in educational domain. Ontologies were developed and merged using protégé 4.0 alpha tools.

Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

“…For this work, the semi-automatic approach is used since through Protégé [50] and its "Refactor>Merge Ontologies" function is carried out the merging of the two ontologies: DC and FOAF. Nevertheless, as Ameen et al [4] mention, this automatic integration does not solve the inconsistencies generated after the process. In consequence, an adjustment is applied to the resulting ontology using the merging algorithm proposed by Ameen et al [4].…”

“…Nevertheless, as Ameen et al [4] mention, this automatic integration does not solve the inconsistencies generated after the process. In consequence, an adjustment is applied to the resulting ontology using the merging algorithm proposed by Ameen et al [4]. This algorithm is illustrated in Figure 2.…”

“…(a) (b) Figure 2 (a) Algorithm to merge ontologies based on the work of Ameen et al [4] and (b) Class hierarchy of the merged ontology Subsequently, the object properties were analyzed in order to identify semantic coincidences, as well as to verify and to match their ranges and domains. Regarding this analysis, the Creator class from DC and Maker class from FOAF were declared as similar.…”

“…Thereby, the main contribution of our work is the designed and implemented semantic approach, which allows KD within academic contents structured with OAI-PMH considering the user's context. Highlighting three specific contributions: the usage of the algorithm for merging ontologies proposed by Ameen et al [4], the adaptation of the students' representation model based on the ontological approach presented by Panagiotopoulos et al [39], and the incorporation of the tool for visualizing information stored in triples developed by Alvarado-Uribe et al [3]. Accordingly, this work also contributes to the four defined rules within the Linked Data area "for publishing, sharing, and interlinking structured data on the Web" [51].…”

A Semantic Model for Selective Knowledge Discovery over OAI-PMH Structured Resources

“…As Ameen, Rahman, and Rani [16] mention, this automatic integration does not resolve the inconsistencies generated after the process. Therefore, the ontology output was subjected to further refinement following the steps of the merging algorithm proposed by the same authors ( Figure 3).…”