Table 4_Ontology-based dietary recommendation system for Chinese children and adolescents:

A pilot validation study evaluated an ontology-based dietary recommendation system using 30 days of school lunch data and 30 days of individual dietary records from middle school students. The system, developed by Zhiyu Zhang, achieved significantly higher Chinese Children Dietary Index (CCDI) scores compared to actual meals. The dataset, last updated in May 2026, is a 711.2 KB document describing the system's development and evaluation.

Use Cases

• Benchmarking dietary recommendation algorithms based on the described ontology and genetic algorithm framework.
• Studying food-group diversity and nutrient adequacy in Chinese children's diets based on the described assessment metrics.
• Developing school or family meal planning tools based on the multi-criteria decision framework described in the study.

Strengths

• Pilot evaluation includes concrete results, such as CCDI score improvements from 103.41 to 117.39 in a group setting.
• System design is detailed, incorporating dietary guidelines, expert knowledge, and a genetic algorithm for optimization.
• Data is shared under a permissive CC-BY-4.0 license.

Limitations

• The primary file is a 711.2 KB DOCX document; the underlying data (e.g., meal plans, food ontology) is not directly provided as a structured dataset.
• Row count and column-level documentation are unknown, limiting suitability assessment for direct ML use.
• The dataset's scale is limited to the described pilot studies involving one primary school and 30 middle school students.

Provenance

Source

figshare, authored by Zhiyu Zhang.

Collection Method

An ontology knowledge base was constructed from dietary guidelines, expert knowledge, and food and meal data.

Time Range

The study period is not specified, but the dataset was updated in 2026.

Freshness

Last updated 2026-05-22 06:10:20; freshness should be verified.

Geography

Data is focused on Chinese children and adolescents.