Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling

• URL: http://arxiv.org/abs/2509.17289v1
• Date: Mon, 22 Sep 2025 00:01:50 GMT
• Title: Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling
• Abstract summary: We introduce CoDe-KG, an end-to-end pipeline for extracting sentence-level knowledge graphs.<n>We contribute a dataset of over 150,000 knowledge triples, which is open source.<n>We show that hybrid chain-of-thought and few-shot prompting yields up to 99.8% exact-match accuracy on sentence simplification.
• Score: 0.39089069256361736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce CoDe-KG, an open-source, end-to-end pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. Using our model, we contribute a dataset of over 150,000 knowledge triples, which is open source. We also contribute a training corpus of 7248 rows for sentence complexity, 190 rows of gold human annotations for co-reference resolution using open source lung-cancer abstracts from PubMed, 900 rows of gold human annotations for sentence conversion policies, and 398 triples of gold human annotations. We systematically select optimal prompt-model pairs across five complexity categories, showing that hybrid chain-of-thought and few-shot prompting yields up to 99.8% exact-match accuracy on sentence simplification. On relation extraction (RE), our pipeline achieves 65.8% macro-F1 on REBEL, an 8-point gain over the prior state of the art, and 75.7% micro-F1 on WebNLG2, while matching or exceeding performance on Wiki-NRE and CaRB. Ablation studies demonstrate that integrating coreference and decomposition increases recall on rare relations by over 20%. Code and dataset are available at https://github.com/KaushikMahmud/CoDe-KG_EMNLP_2025

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