---
title: "Reasoning of Large Language Models over Knowledge Graphs"
description: "To address this, they propose ReKnoS. 10% accuracy. 36 references, cited by 8 papers here."
authors: ["Song Wang", "Junhong Lin", "Xiaojie Guo", "Julian Shun", "Jundong Li", "Yada Zhu"]
year: 2025
venue: "arXiv:2503.22166"
source: "https://arxiv.org/abs/2503.22166"
doi: "10.48550/arxiv.2503.22166"
arxiv: "2503.22166"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2025-reasoning-large-language-models-over/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2025-reasoning-large-language-models-over/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/12/) / Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

# Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

**Authors:** [Song Wang](https://md.research.elamur.ai/free/en/authors/song-wang/), Junhong Lin, Xiaojie Guo, Julian Shun, [Jundong Li](https://md.research.elamur.ai/free/en/authors/jundong-li/) and [Yada Zhu](https://md.research.elamur.ai/free/en/authors/yada-zhu/) · **Year:** 2025 · **Venue:** arXiv:2503.22166 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2503.22166) · [PDF](https://arxiv.org/pdf/2503.22166) · [DOI 10.48550/arxiv.2503.22166](https://doi.org/10.48550/arxiv.2503.22166) · [arXiv 2503.22166](https://arxiv.org/abs/2503.22166) · [OpenAlex](https://openalex.org/W4416422263)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/wang-2025-reasoning-large-language-models-over/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM-based question answering over knowledge graphs, as in Think-on-Graph (ToG), often fails to retrieve any satisfactory reasoning path within the allowed number of hops. When this happens, accuracy drops sharply. The authors examined these failures on GrailQA and found two main causes. The first is that greedy, forward-only search commits early to a plausible but wrong relation. The second is that the needed path is longer than the depth limit.

To address this, they propose ReKnoS. It groups fine-grained KG relations into "super-relations", which are second-level categories in the relation hierarchy, such as music.featured_artist. When a KG has no such hierarchy, relations are clustered by semantic similarity instead. At each step, the LLM scores whole super-relations rather than single edges. This lets one candidate stand for many relation paths, supports both forward and backward reasoning, and widens the search without extra LLM queries. Candidates are restricted to super-relations that actually exist and connect in the KG, which limits hallucination.

Across nine datasets, ReKnoS beats state-of-the-art baselines. It needs fewer LLM calls than ToG, so it offers both better accuracy and lower cost.

## Key points
- **Failure analysis:** ToG on GrailQA (max length 3) fails to retrieve a path for three reasons: misdirection (68.8%), depth limit (25.2%) and path absence (6.0%). Non-retrieved questions lose about 10% accuracy.
- **Overall gains:** On nine real-world datasets, ReKnoS improves accuracy by 2.92% on average. Its retrieval success rate rises by about 25% and its search space grows by about 87%.
- **Accuracy and cost:** On WebQSP, ReKnoS reaches Hits@1 of 81.1 with 7.2 LLM calls, compared with ToG's 76.2 using 15.3 calls. On GrailQA, ReKnoS scores 71.9 versus ToG's 68.7, with 10.2 calls versus 18.9.
- **Clustered super-relations:** With super-relations built by clustering, ReKnoS still leads. It scores 85.3 vs 81.0 on OBQA (ConceptNet) and 92.5 vs 87.2 on MetaQA. It also stays ahead on CWQ and WebQSP after their built-in hierarchy is removed.
- **Scoring rubric and code:** Giving the LLM an explicit relevance-scoring rubric adds small further gains, for example GrailQA rises from 71.2 to 72.5. Code is available at github.com/SongW-SW/REKNOS.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,959 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,789 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,621 works

🧭 Topics (3): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,948 works · [🕸️ Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) · 218 works

## Cite

Cite as: Song Wang, Junhong Lin, Xiaojie Guo, Julian Shun, Jundong Li and Yada Zhu. 2025. Reasoning of Large Language Models over Knowledge Graphs with Super-Relations. arXiv:2503.22166.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wang2025reasoning,
  title = {{Reasoning of Large Language Models over Knowledge Graphs with Super-Relations}},
  author = {Song Wang, Junhong Lin, Xiaojie Guo, Julian Shun, Jundong Li and Yada Zhu},
  year = {2025},
  doi = {10.48550/arxiv.2503.22166},
  eprint = {2503.22166},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.22166}
}
```

- [← previous ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA](https://md.research.elamur.ai/free/en/papers/xinjie-2025-reagent-reversible-multi-agent-reasoning/)
- [next → ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization](https://md.research.elamur.ai/free/en/papers/chen-2025-reform-reflective-autoformalization-prospective-bounded/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/12/)

## References

36 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. GPT4all: Training an assistant-style chatbot with large scale data distillation from GPT-3.5-turbo. GitHub, 2023. [find](https://scholar.google.com/scholar?q=Yuvanesh+Anand%2C+Zach+Nussbaum%2C+Brandon+Duderstadt%2C+Benjamin+Schmidt%2C+and+Andriy+Mulyar%2E+GPT4all%3A+Training+an+assistant%2Dstyle+chatbot+with+large+scale+data+distillation+from+GPT%2D3%2E5%2Dturbo%2E+GitHub%2C+2023)
2. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. [Semantic parsing on freebase from question-answer pairs](https://md.research.elamur.ai/free/en/papers/berant-2013-semantic-parsing-freebase-question-answer/). In Conference on Empirical Methods in Natural Language Processing, pp. 1533–1544, 2013.
3. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In ACM SIGMOD International Conference on Management of Data, pp. 1247–1250, 2008. [find](https://scholar.google.com/scholar?q=Kurt+Bollacker%2C+Colin+Evans%2C+Praveen+Paritosh%2C+Tim+Sturge%2C+and+Jamie+Taylor%2E+Freebase%3A+a+collaboratively+created+graph+database+for+structuring+human+knowledge%2E+In+ACM+SIGMOD+International+Conference+)
4. Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075, 2015. [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Nicolas+Usunier%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+Large%2Dscale+simple+question+answering+with+memory+networks%2E+arXiv+preprint+arXiv%3A1506%2E02075%2C+2015%2E)
5. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in Neural Information Processing Systems, 33:1877–1901, 2020.
6. Mohammad Dehghan, Mohammad Alomrani, Sunyam Bagga, David Alfonso-Hermelo, Khalil Bibi, Abbas Ghaddar, Yingxue Zhang, Xiaoguang Li, Jianye Hao, Qun Liu, Jimmy Lin, Boxing Chen, Prasanna Parthasarathi, Mahdi Biparva, and Mehdi Rezagholizadeh. EWEK-QA : Enhanced web and efficient knowledge graph retrie… [find](https://scholar.google.com/scholar?q=Mohammad+Dehghan%2C+Mohammad+Alomrani%2C+Sunyam+Bagga%2C+David+Alfonso%2DHermelo%2C+Khalil+Bibi%2C+Abbas+Ghaddar%2C+Yingxue+Zhang%2C+Xiaoguang+Li%2C+Jianye+Hao%2C+Qun+Liu%2C+Jimmy+Lin%2C+Boxing+Chen%2C+Prasanna+Parthasarathi%2C+)
7. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. [The Llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783, 2024.
8. Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. [T-REx: A large scale alignment of natural language with knowledge base triples](https://md.research.elamur.ai/free/en/papers/elsahar-2018-t-rex-large-scale-alignment/). In Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 20…
9. Yu Gu, Sue Kase, Michelle Vanni, Brian Sadler, Percy Liang, Xifeng Yan, and Yu Su. Beyond IID: three levels of generalization for question answering on knowledge bases. In Proceedings of the Web Conference, pp. 3477–3488, 2021. [find](https://scholar.google.com/scholar?q=Yu+Gu%2C+Sue+Kase%2C+Michelle+Vanni%2C+Brian+Sadler%2C+Percy+Liang%2C+Xifeng+Yan%2C+and+Yu+Su%2E+Beyond+IID%3A+three+levels+of+generalization+for+question+answering+on+knowledge+bases%2E+In+Proceedings+of+the+Web+Confe)
10. Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023a. [find](https://scholar.google.com/scholar?q=Albert+Q+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+et+al%2E+Mistral+7b)
11. Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. StructGPT: A general framework for large language model to reason over structured data. In Conference on Empirical Methods in Natural Language Processing, pp. 9237–9251, 2023b. [find](https://scholar.google.com/scholar?q=Jinhao+Jiang%2C+Kun+Zhou%2C+Zican+Dong%2C+Keming+Ye%2C+Wayne+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+StructGPT%3A+A+general+framework+for+large+language+model+to+reason+over+structured+data%2E+In+Conference+on+Empirical+Metho)
12. Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Yaliang Li, and Ji-Rong Wen. ReasoningLM: Enabling structural subgraph reasoning in pre-trained language models for question answering over knowledge graph. In Conference on Empirical Methods in Natural Language Processing, pp. 3721–3735, 2023c. [find](https://scholar.google.com/scholar?q=Jinhao+Jiang%2C+Kun+Zhou%2C+Wayne+Xin+Zhao%2C+Yaliang+Li%2C+and+Ji%2DRong+Wen%2E+ReasoningLM%3A+Enabling+structural+subgraph+reasoning+in+pre%2Dtrained+language+models+for+question+answering+over+knowledge+graph%2E+In+)
13. Jinhao Jiang, Kun Zhou, Xin Zhao, and Ji-Rong Wen. UniKGQA: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph. In The Eleventh International Conference on Learning Representations, 2023d. [find](https://scholar.google.com/scholar?q=Jinhao+Jiang%2C+Kun+Zhou%2C+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+UniKGQA%3A+Unified+retrieval+and+reasoning+for+solving+multi%2Dhop+question+answering+over+knowledge+graph%2E+In+The+Eleventh+International+Conference+on+L)
14. Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Yang Song, Chen Zhu, Hengshu Zhu, and Ji-Rong Wen. [KG-Agent: An efficient autonomous agent framework for complex reasoning over knowledge graph](https://md.research.elamur.ai/free/en/papers/jiang-2025-kg-agent-efficient-autonomous-agent/). arXiv preprint arXiv:2402.11163, 2024.
15. Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 7969–7992, 2023e.
16. Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, and Lidong Bing. [Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources](https://md.research.elamur.ai/free/en/papers/li-2023-chain-knowledge-grounding-large-language/). In The Twelfth International Conference on Learning Representations, 2024.
17. Linhao Luo, Yuan-Fang Li, Reza Haf, and Shirui Pan. Reasoning on graphs: Faithful and interpretable large language model reasoning. In The Twelfth International Conference on Learning Representations, 2024. [find](https://scholar.google.com/scholar?q=Linhao+Luo%2C+Yuan%2DFang+Li%2C+Reza+Haf%2C+and+Shirui+Pan%2E+Reasoning+on+graphs%3A+Faithful+and+interpretable+large+language+model+reasoning%2E+In+The+Twelfth+International+Conference+on+Learning+Representations%2C)
18. Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, and Jian Guo. Think-on-Graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval. arXiv preprint arXiv:2407.10805, 2024. — [arXiv:2407.10805](https://arxiv.org/abs/2407.10805) [find](https://scholar.google.com/scholar?q=Shengjie+Ma%2C+Chengjin+Xu%2C+Xuhui+Jiang%2C+Muzhi+Li%2C+Huaren+Qu%2C+and+Jian+Guo%2E+Think%2Don%2DGraph+2%2E0%3A+Deep+and+interpretable+large+language+model+reasoning+with+knowledge+graph%2Dguided+retrieval%2E+arXiv+preprin)
19. Costas Mavromatis and George Karypis. GNN-RAG: Graph neural retrieval for large language model reasoning. arXiv preprint arXiv:2405.20139, 2024. [find](https://scholar.google.com/scholar?q=Costas+Mavromatis+and+George+Karypis%2E+GNN%2DRAG%3A+Graph+neural+retrieval+for+large+language+model+reasoning%2E+arXiv+preprint+arXiv%3A2405%2E20139%2C+2024%2E)
20. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 2381–2391, 01 2018. [find](https://scholar.google.com/scholar?q=Todor+Mihaylov%2C+Peter+Clark%2C+Tushar+Khot%2C+and+Ashish+Sabharwal%2E+Can+a+suit+of+armor+conduct+electricity%3F+a+new+dataset+for+open+book+question+answering%2E+In+Proceedings+of+the+Conference+on+Empirical+M)
21. Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, and Greg Durrett. CREAK: A dataset for commonsense reasoning over entity knowledge. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021. [find](https://scholar.google.com/scholar?q=Yasumasa+Onoe%2C+Michael+J%2E+Q%2E+Zhang%2C+Eunsol+Choi%2C+and+Greg+Durrett%2E+CREAK%3A+A+dataset+for+commonsense+reasoning+over+entity+knowledge%2E+In+Proceedings+of+the+Neural+Information+Processing+Systems+Track+o)
22. OpenAI. ChatGPT: Optimizing language models for dialogue., 2022. [find](https://scholar.google.com/scholar?q=OpenAI%2E+ChatGPT%3A+Optimizing+language+models+for+dialogue%2E%2C+2022%2E)
23. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. [Unifying large language models and knowledge graphs: A roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/). IEEE Transactions on Knowledge and Data Engineering, 2024.
24. Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktaschel, and Sebastian Riedel. [KILT: a benchmark for knowledge intensive language tasks](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/). In NAACL-HLT, pp. 2523–254…
25. Robyn Speer, Joshua Chin, and Catherine Havasi. ConceptNet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017. [find](https://scholar.google.com/scholar?q=Robyn+Speer%2C+Joshua+Chin%2C+and+Catherine+Havasi%2E+ConceptNet+5%2E5%3A+An+open+multilingual+graph+of+general+knowledge%2E+In+Proceedings+of+the+AAAI+conference+on+artificial+intelligence%2C+volume+31%2C+2017%2E)
26. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Sai Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung yeung Shum, and Jian Guo. Think-on-Graph: Deep and responsible reasoning of large language model on knowledge graph. In International Conference on Learning Representations, 2023. [find](https://scholar.google.com/scholar?q=Jiashuo+Sun%2C+Chengjin+Xu%2C+Lumingyuan+Tang%2C+Sai+Wang%2C+Chen+Lin%2C+Yeyun+Gong%2C+Lionel+M%2E+Ni%2C+Heung+yeung+Shum%2C+and+Jian+Guo%2E+Think%2Don%2DGraph%3A+Deep+and+responsible+reasoning+of+large+language+model+on+knowl)
27. Alon Talmor and Jonathan Berant. [The Web as a knowledge-base for answering complex questions](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/). In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 641–651, 2018.
28. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. [Llama 2: Open foundation and fine-tuned chat models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/). arXiv preprint arXiv:2307.09288, 2023.
29. Denny Vrandečić and Markus Krötzsch. [Wikidata: a free collaborative knowledgebase](https://md.research.elamur.ai/free/en/papers/vrandecic-2014-wikidata-free-collaborative-knowledgebase/). Communications of the ACM, 57(10):78–85, 2014.
30. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. [Self-consistency improves chain of thought reasoning in language models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). In The Eleventh International Conference on Learning Representations, 2023.
31. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
32. Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce Ho, and Carl Yang. Knowledge-infused prompting: Assessing and advancing clinical text data generation with large language models. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 15496–15523, 202… [find](https://scholar.google.com/scholar?q=Ran+Xu%2C+Hejie+Cui%2C+Yue+Yu%2C+Xuan+Kan%2C+Wenqi+Shi%2C+Yuchen+Zhuang%2C+Wei+Jin%2C+Joyce+Ho%2C+and+Carl+Yang%2E+Knowledge%2Dinfused+prompting%3A+Assessing+and+advancing+clinical+text+data+generation+with+large+language+)
33. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. [HotpotQA: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Conference on Empirical Methods in Natural Language Processing, 2018.
34. Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. [The value of semantic parse labeling for knowledge base question answering](https://md.research.elamur.ai/free/en/papers/yih-2016-value-semantic-parse-labeling-knowledge/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 201–206, 2016.
35. Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, and Hong Chen. Subgraph retrieval enhanced model for multi-hop knowledge base question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5773–5784,… [find](https://scholar.google.com/scholar?q=Jing+Zhang%2C+Xiaokang+Zhang%2C+Jifan+Yu%2C+Jian+Tang%2C+Jie+Tang%2C+Cuiping+Li%2C+and+Hong+Chen%2E+Subgraph+retrieval+enhanced+model+for+multi%2Dhop+knowledge+base+question+answering%2E+In+Proceedings+of+the+60th+Annu)
36. Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander Smola, and Le Song. Variational reasoning for question answering with knowledge graph. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018. [find](https://scholar.google.com/scholar?q=Yuyu+Zhang%2C+Hanjun+Dai%2C+Zornitsa+Kozareva%2C+Alexander+Smola%2C+and+Le+Song%2E+Variational+reasoning+for+question+answering+with+knowledge+graph%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intellig)

*Updated 3 November 2026*