Skip to content

References

The pages in this site cite foundational papers, recent research on knowledge-graph extraction and graph learning, and a small number of practitioner sources that help explain GraWiki's direction.

Cited work

  • Knowledge graphs as a practical foundation for structured knowledge systems 1.
  • GraphRAG and graph-enhanced retrieval workflows 23.
  • LLM-based extraction of knowledge graphs from text 111213.
  • Heterogeneous graphs, graph representation learning, and message passing 8910.
  • Knowledge graph foundation models and graph reasoning directions 1415.
  • Agent memory and LLM wiki style system design 456.
  • AI engineering as an application-systems discipline 7.

Primary-source note

The Karpathy tweet is cited as a primary-source practitioner statement using the URL supplied in the repository discussion. The documentation quotes the text from the repository transcript because X does not expose a readable non-JavaScript page to the local build environment.

Bibliography

  1. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. Knowledge graphs. ACM Computing Surveys, 54(4):1–37, 2021. doi:10.1145/3447772. ↩

  2. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Sarah Truitt, and Jonathan Larson. From local to global: a graphrag approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. ↩

  3. Shirui Pan, Ling Luo, Yufei Wang, Chuan Chen, Jing Wang, and Xindong Wu. Knowledge graph-enhanced retrieval-augmented generation: a survey. arXiv preprint arXiv:2405.04706, 2024. ↩

  4. Andrej Karpathy. Llm wiki. GitHub Gist, April 2026. Created 2026-04-04. URL: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f. ↩

  5. Andrej Karpathy. Llm knowledge bases. Post on X, April 2026. Posted 2026-04-02. Cited from the user-provided transcript and URL because X requires JavaScript in the local build environment. URL: https://x.com/karpathy/status/2039805659525644595. ↩

  6. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Sarah Truitt, and Jonathan Larson. From local to global: a graphrag approach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024. ↩

  7. Andrew Ng. Ai engineering. DeepLearning.AI talk and essays on building LLM applications, 2024. ↩

  8. Chuan Shi. Heterogeneous graph neural networks. In Graph Neural Networks: Foundations, Frontiers, and Applications, pages 351–369. Springer, Singapore, 2022. ↩

  9. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning, 1263–1272. PMLR, 2017. ↩

  10. William L. Hamilton. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14(3):1–159, 2020. doi:10.2200/S01045ED1V01Y202009AIM046. ↩

  11. Kashif Gillani, Erika Novak, Klemen Kenda, and Dunja Mladenić. Knowledge graph extraction from textual data using llm. Proceedings of Data Mining and Data Warehouses – SiKDD 2024, 2024. doi:10.70314/is.2024.sikdd.15. ↩

  12. Nandana Mihindukulasooriya, Sanju Tiwari, Carlos F. Enguix, and Kusum Lata. Text2kgbench: a benchmark for ontology-driven knowledge graph generation from text. arXiv preprint arXiv:2308.02357, 2023. doi:10.48550/arXiv.2308.02357. ↩

  13. Yutao Zhu, Xinyu Wang, Jiapeng Chen, Shijie Qiao, Yixin Ou, Yushan Yao, Siyang Deng, Huajun Chen, and Ningyu Zhang. Llms for knowledge graph construction and reasoning: recent capabilities and future opportunities. World Wide Web, 2024. doi:10.1007/s11280-024-01297-w. ↩

  14. Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, and Zhaocheng Zhu. Towards foundation models for knowledge graph reasoning. arXiv preprint arXiv:2310.04562, 2024. doi:10.48550/arXiv.2310.04562. ↩

  15. Xingyue Huang, Pablo Barceló, Michael M. Bronstein, İsmail İlkan Ceylan, Mikhail Galkin, Juan L. Reutter, and Miguel Romero Orth. How expressive are knowledge graph foundation models? arXiv preprint arXiv:2502.13339, 2025. doi:10.48550/arXiv.2502.13339. ↩