Developing an Ontological Knowledge Base for Meaningful Search of Fish Transformation Based on Community Wisdom
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Abstract
This paper aims to design and develop a knowledge base and semantic search system for fish processing based on community wisdom. The study involves two groups of samples: (1) 21 individuals engaged in fish processing as either their primary or supplementary occupation, selected through voluntary sampling, and (2) 100 relevant documents on fish processing, chosen based on completeness of knowledge description to be stored in the ontology-based knowledge base. The search efficiency was evaluated using precision, recall, F-measure, mean reciprocal rank (MRR), and sum of squared errors (SSE). The findings indicate that the ontology-based knowledge base for fish processing consists of nine classes. The evaluation results show that assigning keyword weights using correlation coefficient analysis yields an MRR of 0.926 and an SSE of 0.091. Additionally, document similarity measurement for fish processing knowledge reveals that Method A achieves an F-measure of 0.8283, outperforming Method B, which has an F-measure of 0.6749. However, Method A has a lower mean precision of 0.7839 compared to Method B, which achieves 0.8324, indicating that Method B provides better search accuracy.
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