Evaluation Scores Dataset: LLM and Human Assessment of Halal Tourism Websites for Knowledge Graph Construction

This dataset contains the raw evaluation scores used in the research paper "Enhancing Knowledge Graph Construction with Automated Source Evaluation Using Large Language Models" published in the Journal of Universal Computer Science (2024). The data represents comparative evaluations of Halal tourism websites in Japan, assessed by both Large Language Models (LLMs) and human experts.The dataset includes:LLM evaluation scores across multiple criteria (credibility, relevance, content quality, coverage, comprehensiveness, and accessibility) for four Halal tourism websites, using various LLM models including GPT-3.5-turbo, GPT-4, Google Gemini Pro, and Mixtral-8x7B-Instruct-v0.1Human expert evaluation scores from 22 respondents using the same criteria frameworkThese evaluations were conducted using a weighted criteria system (credibility: 30%, relevance: 24%, content quality: 21%, coverage: 8%, comprehensiveness: 8%, accessibility: 9%) to assess the suitability of websites as knowledge sources for constructing domain-specific knowledge graphs.The data underpins the comparative analysis between LLM and human evaluations, demonstrating the effectiveness of automated source evaluation in knowledge graph construction. This dataset can be valuable for researchers studying automated website evaluation, knowledge graph construction, or the application of LLMs in quality assessment tasks.