@inproceedings{acharya-etal-2026-ontology,
title = "Ontology-Guided Knowledge Graph Construction from Structured Metadata and Climate Science Publications",
author = "Acharya, Aayush and
Zhang, Qi and
Dragut, Eduard and
Latecki, Longin Jan",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.252/",
pages = "5615--5642",
ISBN = "979-8-89176-439-2",
abstract = "The rapid expansion of scientific literature far outpaces the speed of manual curation, creating a fundamental bottleneck in extracting structured knowledge from scientific texts. To address this, we introduce an agentic, ontology-guided framework for knowledge graph (KG) construction. Our framework utilizes specialized agents for document understanding, information extraction, ontology conformance, and entity linking. First, an LLM extracts factual knowledge in the form of triples with explicit textual evidence from research papers. Then the triplets undergo a multistage verification process to ground the triplets in the ontology. By enforcing schema constraints and requiring evidence in the form of text fragments, we ensure the data is both semantically consistent and traceable. In an agreement study over 1,000 extracted triplets, Gemini-2.5-flash achieves Cohen{'}s $\kappa$ scores of 0.640{--}0.700 against three human annotators, matching the human{--}human agreement range of 0.616{--}0.689. We applied this framework to 2,000 scientific publications and construct a novel, ontology-grounded KG for climate science with 133,990 nodes and 755,308 relations; of the 23,327 triplets that the publications contribute after validation, 98.96{\%} are judged correct. The pipeline code and KG is publicly available."
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%0 Conference Proceedings
%T Ontology-Guided Knowledge Graph Construction from Structured Metadata and Climate Science Publications
%A Acharya, Aayush
%A Zhang, Qi
%A Dragut, Eduard
%A Latecki, Longin Jan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F acharya-etal-2026-ontology
%X The rapid expansion of scientific literature far outpaces the speed of manual curation, creating a fundamental bottleneck in extracting structured knowledge from scientific texts. To address this, we introduce an agentic, ontology-guided framework for knowledge graph (KG) construction. Our framework utilizes specialized agents for document understanding, information extraction, ontology conformance, and entity linking. First, an LLM extracts factual knowledge in the form of triples with explicit textual evidence from research papers. Then the triplets undergo a multistage verification process to ground the triplets in the ontology. By enforcing schema constraints and requiring evidence in the form of text fragments, we ensure the data is both semantically consistent and traceable. In an agreement study over 1,000 extracted triplets, Gemini-2.5-flash achieves Cohen’s ąppa scores of 0.640–0.700 against three human annotators, matching the human–human agreement range of 0.616–0.689. We applied this framework to 2,000 scientific publications and construct a novel, ontology-grounded KG for climate science with 133,990 nodes and 755,308 relations; of the 23,327 triplets that the publications contribute after validation, 98.96% are judged correct. The pipeline code and KG is publicly available.
%U https://aclanthology.org/2026.aacl-long.252/
%P 5615-5642
Markdown (Informal)
[Ontology-Guided Knowledge Graph Construction from Structured Metadata and Climate Science Publications](https://aclanthology.org/2026.aacl-long.252/) (Acharya et al., AACL-IJCNLP 2026)
ACL