@inproceedings{bai-etal-2026-autoschemakg,
title = "{A}uto{S}chema{KG}: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora",
author = "Bai, Jiaxin and
Fan, Wei and
Hu, Qi and
Zong, Qing and
Li, Chunyang and
Tsang, Hong Ting and
Luo, Hongyu and
Yim, Yauwai and
Huang, Haoyu and
Zhou, Xiao and
Qin, Feng and
Zheng, Tianshi and
Peng, Xi and
Yao, Xin and
Yang, Huiwen and
Wu, Leijie and
Yi, JI and
Zhang, Gong and
Chen, Renhai and
Song, Yangqiu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.942/",
pages = "20557--20584",
ISBN = "979-8-89176-390-6",
abstract = "We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize instances into semantic categories. Processing over 50 million documents, we construct ATLAS (Automated Triple Linking And Schema induction), a family of knowledge graphs with 900+ million nodes and 5.9 billion edges. This approach outperforms state-of-the-art baselines on multi-hop QA tasks and enhances LLM factuality. Notably, our schema induction achieves 92{\%} semantic alignment with human-crafted schemas with zero manual intervention, demonstrating that billion-scale knowledge graphs with dynamically induced schemas can effectively complement parametric knowledge in large language models."
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<abstract>We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize instances into semantic categories. Processing over 50 million documents, we construct ATLAS (Automated Triple Linking And Schema induction), a family of knowledge graphs with 900+ million nodes and 5.9 billion edges. This approach outperforms state-of-the-art baselines on multi-hop QA tasks and enhances LLM factuality. Notably, our schema induction achieves 92% semantic alignment with human-crafted schemas with zero manual intervention, demonstrating that billion-scale knowledge graphs with dynamically induced schemas can effectively complement parametric knowledge in large language models.</abstract>
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%0 Conference Proceedings
%T AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
%A Bai, Jiaxin
%A Fan, Wei
%A Hu, Qi
%A Zong, Qing
%A Li, Chunyang
%A Tsang, Hong Ting
%A Luo, Hongyu
%A Yim, Yauwai
%A Huang, Haoyu
%A Zhou, Xiao
%A Qin, Feng
%A Zheng, Tianshi
%A Peng, Xi
%A Yao, Xin
%A Yang, Huiwen
%A Wu, Leijie
%A Yi, J. I.
%A Zhang, Gong
%A Chen, Renhai
%A Song, Yangqiu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F bai-etal-2026-autoschemakg
%X We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously extract knowledge triples and induce comprehensive schemas directly from text, modeling both entities and events while employing conceptualization to organize instances into semantic categories. Processing over 50 million documents, we construct ATLAS (Automated Triple Linking And Schema induction), a family of knowledge graphs with 900+ million nodes and 5.9 billion edges. This approach outperforms state-of-the-art baselines on multi-hop QA tasks and enhances LLM factuality. Notably, our schema induction achieves 92% semantic alignment with human-crafted schemas with zero manual intervention, demonstrating that billion-scale knowledge graphs with dynamically induced schemas can effectively complement parametric knowledge in large language models.
%U https://aclanthology.org/2026.acl-long.942/
%P 20557-20584
Markdown (Informal)
[AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://aclanthology.org/2026.acl-long.942/) (Bai et al., ACL 2026)
ACL
- Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, and Yangqiu Song. 2026. AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20557–20584, San Diego, California, United States. Association for Computational Linguistics.