The Relation between the Combination Levels in the Structure of Concepts Entities and the Amount of Semantic Relations in Improving Information Retrieval Performance in UN, FAO’s Agricultural Ontology

Author

Abstract

This research aims to identify the relations between the combination levels in concepts structure with the amount of semantic relations through applying quantitative evaluation. Research hypotheses emphasize that there is a significant relations between the level of combination in the structure of concepts and the number of their semantic relationships. The results of the research background showed that there is no investigation on the relations between structure of concepts with different number of words in their structure and the semantic relations to increase IR performance. Research data are extracted from VocBench as an authoritative agricultural ontology, produced by Food and Agriculture Organization (FAO), United Nations. The size of the research population is 40,000 agricultural concepts. The sample size is 1,500 concepts selected through stratified random sampling. The analysis of the combination levels of concepts structure has been applied using Excel and SPSS software to operate Proportional Analysis and Frequency Analysis. The relations between concepts structure and semantic relations have been adapted inferential statistics, especially Compare Mean Analysis and Pearson Correlation method using SPSS software reports. The research results show that the results of Factoring Ratio is equal 0.58 between one and two-word concepts, meaning that simple concepts comprise of the dominant domain in ontology. The amount of two-word concepts includes 38.7% of the total concepts. The number of two-word concepts causes decrease of simplicity in concept structure. The average of semantic relations input and output in simple concepts is equal to 6.70 or 7. There is a significant inverse relations between the combination level in concepts structure and the number of taxonomic relations (p-value = 0.000, N = 1500 and r = -0.98) and the number of non-taxonomic relations (p-value = 0.035, N = 1500 and r= -0.54) based on Pearson correlation. It means that an increase in the number of words in concepts structure results in decreasing the number of semantic relations. The results showed that reducing the level of composition in the structure of concepts increases the semantic relations which results in increasing recall and precision in IR performance as well as increasing the integration of the conceptual relations network in ontology. The research results can be used in developing and evaluating the structure of knowledge organization systems (KOSs) such as thesauri regarding structural analysis in IR performance as well as in constructing agricultural ontology of Iran.

Keywords


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