Open Local Knowledge Graph Construction from Academic Papers Using Generative Large Language Models

This manuscript introduces paper2lkg, a novel Local Knowledge Graph Construction (KGC) pipeline designed to transform individual academic papers into their structured local Knowledge Graph (KG) representations.The pipeline harnesses Large Language Models (LLMs), particularly generative LLMs, to automate key Natural Language Processing (NLP) tasks in KGC.The constructed local KGs can potentially be used to enrich an existing academic KG that lacks detailed local representations of individual papers or further integrated into new academic KGs through Knowledge Graph Alignment (KGA).

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3717820
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48
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article

Open Local Knowledge Graph Construction from Academic Papers Using Generative Large Language Models

Pouya Ghiasnezhad Omran, Haoting Chen, Sergio José Rodríguez Méndez
1 citations
Topic Modeling
2.48
article

Open Local Knowledge Graph Construction from Academic Papers Using Generative Large Language Models

Pouya Ghiasnezhad Omran, Haoting Chen, Sergio José Rodríguez Méndez
article en
1 citations

Abstract

This manuscript introduces paper2lkg, a novel Local Knowledge Graph Construction (KGC) pipeline designed to transform individual academic papers into their structured local Knowledge Graph (KG) representations.The pipeline harnesses Large Language Models (LLMs), particularly generative LLMs, to automate key Natural Language Processing (NLP) tasks in KGC.The constructed local KGs can potentially be used to enrich an existing academic KG that lacks detailed local representations of individual papers or further integrated into new academic KGs through Knowledge Graph Alignment (KGA).

Australian National University (AU)
Openalex Percentile: Top 10%
Topic Modeling
2.48
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