ChenAI2015/A-Dataset-for-Complex-Reasoning-over-Textual-Knowledge-Graphs-in-Medicine
RiTeK: Medical Textual Knowledge Graph QA Benchmark RiTeK is a benchmark for complex reasoning over medical Textual Knowledge Graphs (medical TKGs). It evaluates whether retrieval systems and Large Language Models (LLMs) can answer realistic medical questions by using both relational paths and textual entity descriptions. Dataset Overview The benchmark contains three medical graph QA subsets: Dataset Directory Splits KG file ADint Adint train / dev /… See the full description on the dataset page: https://huggingface.co/datasets/ChenAI2015/A-Dataset-for-Complex-Reasoning-over-Textual-Knowledge-
| Author / organization | ChenAI2015 |
|---|---|
| Downloads | 157 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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- 16 Oct 2026 Description: RiTeK: Medical Textual Knowledge Graph QA Benchmark RiTeK is a benchmark for complex reasoning over medical Textual Knowledge Graphs (medical TKGs). It evaluates whether retrieval systems and Large Language Models (LLMs) can answer realisti → RiTeK: Medical Textual Knowledge Graph QA Benchmark RiTeK is a benchmark for complex reasoning over medical Textual Knowledge Graphs (medical TKGs). It evaluates whether retrieval systems and Large Language Models (LLMs) can answer realisti
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