STRUCTURAL ANALYSIS OF SEMANTIC RELATIONS REGARDING INTEGRATION AND ASSOCIATION OF SEMANTIC NETWORK IN VOCBENCH AS AN AGRICULTURAL ONTOLOGY

  • Maziar Amirhosseini Agricultural Research, Education and Extension Organization (AREEO), Yaman Ave., Chamran Highway, Garden of Agriculture, Tehran, Iran
  • Juhana Salim Faculty of Information Science and Technology and Centre for Collaborative Innovation, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
Keywords: Ontology Evaluation, Structural Analysis, Semantic Relations, Integration Ratio, Relativeness Ratio, Agricultural Ontology, Vocbench

Abstract

The purpose of this article is to analyze semantic relations based on graph-independent structural analysis in VocBench. The mix-method of deductive and inductive approach is adapted in operating the research methodology, especially for data collection. The research data are structural domains of semantic relations in ontologies. The data resource is the authoritative agricultural ontology, VocBench, that has been originated by Food and Agricultural organization (FAO), United Nation. VocBench includes around 40000 concepts. The sample size is around 1500 concepts. Sampling technique used is the stratified random sampling. The data analysis results are employed in the SPSS and Excel software using descriptive and proportional analysis. The research results reveal that the taxonomic relations cover a wide area in VocBench. Moreover, the overloading was not seen in the usage of non-taxonomic relations. The high frequency in the usage of the semantic relations’ output might be implied the possibility of the width (i.e., exhaustivity) in semantic network in VocBench.

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Published
2019-05-31
How to Cite
Amirhosseini, M., & Salim, J. (2019). STRUCTURAL ANALYSIS OF SEMANTIC RELATIONS REGARDING INTEGRATION AND ASSOCIATION OF SEMANTIC NETWORK IN VOCBENCH AS AN AGRICULTURAL ONTOLOGY . International Journal of Engineering Technologies and Management Research, 6(5), 41-57. https://doi.org/10.29121/ijetmr.v6.i5.2019.371