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arxiv: 2410.13987 · v3 · submitted 2024-10-17 · 💻 cs.CL

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

Jiatan Huang , Mingchen Li , Zonghai Yao , Dawei Li , Yuxin Zhang , Zhichao Yang , Yongkang Xiao , Feiyun Ouyang
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Xiaohan Li Shuo Han Hong Yu
This is my paper

Pith reviewed 2026-05-23 18:24 UTC · model grok-4.3

classification 💻 cs.CL
keywords textual knowledge graphsmedical domainLLM retrievalcomplex reasoningbenchmark datasetsynthesized queriesknowledge graph retrieval
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The pith

The RiTeK dataset reveals that current LLM-based retrievers struggle with complex queries over medical textual knowledge graphs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates RiTeK to fill gaps in medical textual knowledge graphs by providing queries that combine multiple graph topologies, relations, and detailed text. Queries are generated and then checked by medical experts to ensure they reflect realistic use cases. When eleven existing retrievers are tested on the benchmark, they all show clear weaknesses in handling the required reasoning. This setup matters because better retrieval from such graphs could let language models follow relational paths to answer harder medical questions accurately.

Core claim

We develop the RiTeK dataset for LLMs complex reasoning over medical textual knowledge graphs, covering a broad range of topological structures through synthesized realistic user queries that integrate diverse topological structures, relational information, and complex textual descriptions. After a rigorous medical expert evaluation, RiTeK serves as a benchmark where eleven representative retrievers struggle, revealing notable limitations in current LLM-driven retrieval approaches for semi-structured medical data.

What carries the argument

The RiTeK dataset, which synthesizes queries that combine diverse topological structures and relational paths within medical textual knowledge graphs.

If this is right

  • Retrieval systems tailored to semi-structured medical data are needed to overcome current limitations.
  • Relational path information from textual knowledge graphs can enhance LLM inference on complex medical questions.
  • Existing LLM-driven retrievers require substantial improvement before reliable use on medical TKGs.
  • New evaluation benchmarks like RiTeK can expose gaps not visible in simpler retrieval tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Developers of medical AI tools may need to design retrieval modules that explicitly track graph topology rather than relying on text similarity alone.
  • The benchmark approach could be adapted to test retrieval in other domains that use semi-structured records, such as legal or financial knowledge bases.
  • Poor results across multiple retrievers suggest that simply increasing model size or training data may not close the gap without changes to how graph structure is represented.

Load-bearing premise

The expert-reviewed synthesized queries match the distribution and difficulty of real-world medical user queries over textual knowledge graphs.

What would settle it

A direct comparison of retriever accuracy on RiTeK versus accuracy on a held-out set of actual, non-synthesized clinical queries from medical records or forums.

Figures

Figures reproduced from arXiv: 2410.13987 by Dawei Li, Feiyun Ouyang, Hong Yu, Jiatan Huang, Mingchen Li, Shuo Han, Xiaohan Li, Yongkang Xiao, Yuxin Zhang, Zhichao Yang, Zonghai Yao.

Figure 1
Figure 1. Figure 1: An example demonstrating the input, expected [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The process of constructing textual structured retrieval datasets involves five main steps, 1) Relational [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Expansion progress of MCTS and Relational [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The extract match F1 performance of Rela [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The extract match F1 performance of Relati [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of query lengths and answer [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces RiTeK, a dataset for complex reasoning over medical textual knowledge graphs (TKGs). It synthesizes queries that integrate diverse topological structures, relational paths, and textual descriptions; subjects them to medical expert validation; and benchmarks 11 retrievers, reporting that existing LLM-driven methods struggle and exhibit notable limitations for semi-structured medical data.

Significance. If the queries prove representative, the benchmark could usefully expose gaps in current retrieval approaches for medical TKGs and motivate targeted improvements. The dataset itself may become a reusable resource for evaluating reasoning over semi-structured medical knowledge.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Query Synthesis): the claim that expert validation produces realistic queries is load-bearing for the benchmarking conclusions, yet no quantitative metrics (inter-expert agreement, number of reviewers, disagreement resolution, or query statistics) are reported.
  2. [§4 and §5] §4 (Benchmarking) and §5 (Discussion): the headline finding that the 11 retrievers reveal 'notable limitations' rests on the assumption that RiTeK queries match real medical query distributions in difficulty and topology; no comparison to query logs or distributional statistics (path length, entity co-occurrence, etc.) is provided.
minor comments (1)
  1. [Abstract] Abstract: 'Dataset1' appears to be a typographical placeholder and should be replaced with the dataset name.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Query Synthesis): the claim that expert validation produces realistic queries is load-bearing for the benchmarking conclusions, yet no quantitative metrics (inter-expert agreement, number of reviewers, disagreement resolution, or query statistics) are reported.

    Authors: We agree that the absence of quantitative metrics on the expert validation process is a limitation. In the revised manuscript we will report the number of medical experts involved, inter-expert agreement statistics (e.g., Cohen’s kappa), the procedure used to resolve disagreements, and additional query-level statistics. These details will be added to Section 3. revision: yes

  2. Referee: [§4 and §5] §4 (Benchmarking) and §5 (Discussion): the headline finding that the 11 retrievers reveal 'notable limitations' rests on the assumption that RiTeK queries match real medical query distributions in difficulty and topology; no comparison to query logs or distributional statistics (path length, entity co-occurrence, etc.) is provided.

    Authors: RiTeK is constructed to systematically incorporate diverse topological structures, multi-hop relational paths, and complex textual descriptions that are characteristic of challenging medical reasoning tasks, rather than to replicate the exact distribution of real-world clinical query logs. Such logs are typically unavailable for public use due to patient privacy constraints. The observed performance gaps on RiTeK therefore highlight limitations that would be expected to appear when retrievers encounter queries with comparable structural complexity. We will revise §5 to clarify this scope and explicitly discuss the synthetic nature of the benchmark without asserting distributional equivalence to real logs. revision: no

Circularity Check

0 steps flagged

No circularity: dataset synthesis plus external benchmarking

full rationale

The paper constructs RiTeK by synthesizing queries from topological structures, relational paths and textual descriptions, applies expert validation, and then evaluates 11 independent retrievers. No equations, fitted parameters presented as predictions, self-definitional steps, or load-bearing self-citations appear. Benchmark results are external to the synthesis process and do not reduce to the paper's own inputs by construction. Representativeness of synthesized queries is a validity issue, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim that existing retrievers have notable limitations rests on the untested premise that the synthetic queries are representative of real medical use cases and that expert review is sufficient validation.

axioms (2)
  • domain assumption Synthesized queries integrating diverse topological structures, relational information, and complex textual descriptions can stand in for real medical user queries.
    Invoked in the dataset construction step described in the abstract.
  • domain assumption Rigorous medical expert evaluation process adequately assesses and validates query quality.
    Invoked to justify the benchmark's reliability.

pith-pipeline@v0.9.0 · 5778 in / 1232 out tokens · 33915 ms · 2026-05-23T18:24:16.625757+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

128 extracted references · 128 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    Measuring Massive Multitask Language Understanding

    Measuring massive multitask language under- standing. arXiv preprint arXiv:2009.03300. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Ap- plied Sciences, 11(14):6421. Qiao Jin, Bhuwan Dhingra, Zhengping...

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  2. [2]

    arXiv preprint arXiv:2402.10779

    A condensed transition graph framework for zero-shot link prediction with large language models. arXiv preprint arXiv:2402.10779. P Rajpurkar. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250. Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 201...

    work page arXiv 2016

  3. [3]

    Transactions of the Association for Computational Linguistics, 6:287– 302

    Constructing datasets for multi-hop reading comprehension across documents. Transactions of the Association for Computational Linguistics, 6:287– 302. Shirley Wu, Shiyu Zhao, Qian Huang, Kexin Huang, Michihiro Yasunaga, Kaidi Cao, Vassilis N Ioan- nidis, Karthik Subbian, Jure Leskovec, and James Zou. 2024a. Avatar: Optimizing llm agents for tool-assisted ...

    work page arXiv 2024

  4. [4]

    arXiv preprint arXiv:2410.01553

    Medqa-cs: Benchmarking large language mod- els clinical skills using an ai-sce framework. arXiv preprint arXiv:2410.01553. Wen Tau Yih, Matthew Richardson, Chris Meek, Ming Wei Chang, and Jina Suh. 2016. The value of semantic parse labeling for knowledge base ques- tion answering. In Proceedings of the 54th Annual Meeting of the Association for Computatio...

    work page arXiv 2016

  5. [5]

    Gene -> [Production by cell population] -> Gene

    work page

  6. [6]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene

    work page

  7. [7]

    Gene -> [Relationships involving regulation and pathways] -> Gene

    work page

  8. [8]

    Gene -> [Binding, ligand] -> Gene

    work page

  9. [9]

    Gene -> [Affects expression/production] -> Gene

    work page

  10. [10]

    Gene -> [Gene-Gene] -> Gene

    work page

  11. [11]

    Chemical -> [Chemical-Chemical] -> Chemi- cal

    work page

  12. [12]

    Disease -> [Ancestors of disease] -> Disease

    work page

  13. [13]

    Disease -> [Associations between diseases] -> Disease

    work page

  14. [14]

    Gene -> [Interactions] -> Chemical

    work page

  15. [15]

    Chemical -> [Interactions] -> Gene

    work page

  16. [16]

    Gene -> [Interactions] -> Gene

    work page

  17. [17]

    Gene -> [Interactions] -> Disease

    work page

  18. [18]

    Gene -> [Drug targets] -> Disease

    work page

  19. [19]

    Gene -> [Role in pathogenesis, or promotes progression] -> Disease

    work page

  20. [20]

    Gene -> [Mutations affect, or polymorphisms alter risk] -> Disease

    work page

  21. [21]

    Disease -> [Biomarkers (diagnostic), or regu- lation linked to disease] -> Gene

    work page

  22. [22]

    Disease -> [Overexpression in disease] -> Gene

    work page

  23. [23]

    Chemical -> [Treatment or therapy] -> Dis- ease

    work page

  24. [24]

    Chemical -> [Side effect or adverse event] -> Disease

    work page

  25. [25]

    Chemical -> [Inhibits cell growth] -> Disease

    work page

  26. [26]

    Chemical -> [Role in pathogenesis] -> Dis- ease

    work page

  27. [27]

    Chemical -> [Prevents, suppresses, or allevi- ates, reduces] -> Disease

    work page

  28. [28]

    Disease -> [Biomarkers (progression)] -> Chemical

    work page

  29. [29]

    Chemical -> [Agonism, activation, or antago- nism, blocking] -> Gene

    work page

  30. [30]

    Chemical -> [Binding, ligand] -> Gene

    work page

  31. [31]

    Chemical -> [Affects expression/production] -> Gene

    work page

  32. [32]

    Chemical -> [Inhibits] -> Gene

    work page

  33. [33]

    Gene -> [Transport, channels] -> Chemical

    work page

  34. [34]

    Gene -> [Metabolism, pharmacokinetics] -> Chemical

    work page

  35. [35]

    Gene -> [Enzyme activity] -> Chemical

    work page

  36. [36]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Drug targets] -> Disease

    work page

  37. [37]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Role in pathogenesis, or promotes progression] -> Disease

    work page

  38. [38]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Mutations affect, or poly- morphisms alter risk] -> Disease

    work page

  39. [39]

    Gene -> [Relationships involving regulation and pathways] -> Gene -> [Binding, ligand] -> Gene

    work page

  40. [40]

    Gene -> [Binding, ligand] -> Gene -> [Affects expression/production] -> Gene

    work page

  41. [41]

    Gene -> [Interactions] -> Gene -> [Interac- tions] -> Chemical

    work page

  42. [42]

    Gene -> [Transport, channels] -> Chemical -> [Agonism, activation, or antagonism, block- ing] -> Gene

    work page

  43. [44]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Enhance response, or acti- vate, stimulate] -> Gene

    work page

  44. [45]

    Gene -> [Interactions] -> Chemical -> [Treat- ment or therapy] -> Disease

    work page

  45. [46]

    Gene -> [Interactions] -> Chemical -> [Side effect or adverse event] -> Disease

    work page

  46. [47]

    Gene -> [Interactions] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene

    work page

  47. [48]

    Chemical -> [Treatment or therapy] -> Dis- ease -> [Biomarkers (diagnostic), or regula- tion linked to disease] -> Gene

    work page

  48. [49]

    Disease -> [Associations between diseases] -> Disease -> [Ancestors of disease] -> Disease

    work page

  49. [50]

    Disease -> [Biomarkers (diagnostic), or reg- ulation linked to disease] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene

    work page

  50. [51]

    Gene -> [Interactions] -> Gene -> [Transport, channels] -> Chemical

    work page

  51. [52]

    Gene -> [Metabolism, pharmacokinetics] -> Chemical -> [Binding, ligand] -> Gene

    work page

  52. [53]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Drug targets] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene

    work page

  53. [55]

    Gene -> [Transport, channels] -> Chemical -> [Agonism, activation, or antagonism, block- ing] -> Gene -> [Binding, ligand] -> Chemical

    work page

  54. [56]

    Gene -> [Metabolism, pharmacokinetics] -> Chemical -> [Binding, ligand] -> Gene -> [In- hibits] -> Chemical

    work page

  55. [57]

    Gene -> [Interactions] -> Chemical -> [Treat- ment or therapy] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene

    work page

  56. [58]

    Gene -> [Interactions] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene -> [Transport, channels] -> Chemical

    work page

  57. [59]

    Gene -> [Role in pathogenesis, or promotes progression] -> Disease -> [Biomarkers (di- agnostic), or regulation linked to disease] -> Gene -> [Metabolism, pharmacokinetics] -> Chemical

    work page

  58. [60]

    Chemical -> [Agonism, activation, or antago- nism, blocking] -> Gene -> [Drug targets] -> Disease -> [Biomarkers (diagnostic), or regu- lation linked to disease] -> Gene

    work page

  59. [61]

    Disease -> [Biomarkers (diagnostic), or reg- ulation linked to disease] -> Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene -> [Role in pathogenesis, or promotes progression] -> Disease

    work page

  60. [62]

    Disease -> [Biomarkers (diagnostic), or regulation linked to disease] -> Gene -> [Metabolism, pharmacokinetics] -> Chemical -> [Side effect or adverse event] -> Disease

    work page

  61. [63]

    Gene -> [Production by cell population] -> Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Relationships involving regulation and pathways] -> Gene

    work page

  62. [64]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Binding, ligand] -> Gene -> [Affects expression/production] -> Gene

    work page

  63. [65]

    Gene -> [Relationships involving regulation and pathways] -> Gene -> [Gene-Gene] -> Gene -> [Binding, ligand] -> Gene

    work page

  64. [66]

    Gene -> [Interactions] -> Gene -> [Interac- tions] -> Gene -> [Transport, channels] -> Chemical

    work page

  65. [67]

    Gene -> [Interactions] -> Gene -> [Interac- tions] -> Gene -> [Metabolism, pharmacoki- netics] -> Chemical

    work page

  66. [68]

    Gene -> [Enhance response, or activate, stim- ulate] -> Gene -> [Mutations affect, or poly- morphisms alter risk] -> Disease -> [Overex- pression in disease] -> Gene

    work page

  67. [69]

    Gene -> [Enzyme activity] -> Chemical -> [Affects expression/production] -> Gene -> [Chemical-Chemical] -> Chemical

    work page

  68. [70]

    Gene -> [Interactions] -> Chemical -> [Role in pathogenesis] -> Disease -> [Overexpres- sion in disease] -> Gene

    work page

  69. [71]

    Chemical -> [Side effect or adverse event] -> Disease -> [Biomarkers (diagnostic), or reg- ulation linked to disease] -> Gene -> [Muta- tions affect, or polymorphisms alter risk] -> Disease

    work page

  70. [72]

    Chemical -> [Inhibits cell growth] -> Dis- ease -> [Overexpression in disease] -> Gene -> [Role in pathogenesis, or promotes progres- sion] -> Disease C.2 RiTeK-ADint

    work page

  71. [73]

    Amino Acid, Peptide, or Protein -> [affects] -> Cell Function

    work page

  72. [74]

    Amino Acid, Peptide, or Protein -> [affects] -> Disease or Syndrome

    work page

  73. [75]

    Amino Acid, Peptide, or Protein -> [causes] -> Anatomical Abnormality

    work page

  74. [76]

    Amino Acid, Peptide, or Protein -> [interacts with] -> Pharmacologic Substance

    work page

  75. [77]

    Anatomical Abnormality -> [affects] -> Organ or Tissue Function

    work page

  76. [78]

    Anatomical Abnormality -> [complicates] -> Disease or Syndrome

    work page

  77. [79]

    Anatomical Abnormality -> [manifestation of] -> Genetic Function

    work page

  78. [80]

    Antibiotic -> [affects] -> Molecular Function

    work page

  79. [81]

    Antibiotic -> [causes] -> Pathologic Function

    work page

  80. [82]

    Antibiotic -> [disrupts] -> Cell Component

    work page

Showing first 80 references.