RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine

[2410.13987] RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2410.13987 (cs) [Submitted on 17 Oct 2024 ( v1 ), last revised 11 Apr 2026 (this version, v3)] Title: RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine Authors: Jiatan Huang , Mingchen Li , Zonghai Yao , Dawei Li , Yuxin Zhang , Zhichao Yang , Yongkang Xiao , Feiyun Ouyang , Xiaohan Li , Shuo Han , Hong Yu View a PDF of the paper titled RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine, by Jiatan Huang and 10 other authors View PDF HTML (experimental) Abstract: Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs), as the relational path information from TKGs could enhance the inference ability of Large Language Models (LLMs). However, the main bottlenecks lie in the scarcity of existing medical TKGs, the limited expressiveness of their topological structures, and the lack of comprehensive evaluations of current retrievers for medical TKGs. To address these challenges, we first develop a Dataset1 for LLMs Complex Reasoning over medical Textual Knowledge Graphs (RiTeK), covering a broad range of topological structures. Specifically, we synthesize realistic user queries integrating diverse topological structures, relational information, and complex textual descriptions. We conduct a rigorous medical expert evaluation process to assess and validate the quality of our synthesized queries. RiTeK also serves as a comprehensive benchmark dataset for evaluating the capabilities of retrieval systems built upon LLMs. By assessing 11 representative retrievers on this benchmark, we observe that existing methods struggle to perform well, revealing notable limitations in current LLM-driven retrieval approaches. These findings highlight the pressing need for more effective retrieval systems tailored for semi-structured data in the medical domain. Comments: ACL 2026 Findings Subjects: Computation and Language (cs.CL) Cite as: arXiv:2410.13987 [cs.CL] (or arXiv:2410.13987v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2410.13987 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mingchen Li [ view email ] [v1] Thu, 17 Oct 2024 19:33:37 UTC (1,344 KB) [v2] Sat, 3 Jan 2026 19:04:11 UTC (569 KB) [v3] Sat, 11 Apr 2026 14:40:49 UTC (569 KB) Full-text links: Access Paper: View a PDF of the paper titled RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine, by Jiatan Huang and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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