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Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction

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arxiv 2307.01128 v1 pith:22PB6NGK submitted 2023-07-03 cs.CL cs.AI

Salvatore Carta , Alessandro Giuliani , Leonardo Piano , Alessandro Sebastian Podda , Livio Pompianu , Sandro Gabriele Tiddia This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgegraphapproachgenerationmainzero-shotconstructiondifferent
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In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios. In this context, knowledge graphs represent a potent tool for retrieving and organizing a vast amount of information in a properly interconnected and interpretable structure. However, their generation is still challenging and often requires considerable human effort and domain expertise, hampering the scalability and flexibility across different application fields. This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models, such as GPT-3.5, that can address all the main critical issues in knowledge graph building. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies in the main stages of the generation process. Our unique manifold approach may encompass significant benefits to the scientific community. In particular, the main contribution can be summarized by: (i) an innovative strategy for iteratively prompting large language models to extract relevant components of the final graph; (ii) a zero-shot strategy for each prompt, meaning that there is no need for providing examples for "guiding" the prompt result; (iii) a scalable solution, as the adoption of LLMs avoids the need for any external resources or human expertise. To assess the effectiveness of our proposed model, we performed experiments on a dataset that covered a specific domain. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.

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Forward citations

Cited by 7 Pith papers

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    cs.HC 2025-02 conditional novelty 6.0 of 10

    A chatbot interview pipeline with AI-assisted coding and causal knowledge graphs can elicit and decompose depression stigma at scale.

  2. Hidden Entity Detection from GitHub Leveraging Large Language Models

    cs.CL 2025-01 conditional novelty 6.0 of 10

    Few-shot Llama 2 7B and Mistral 7B models detect and classify dataset and software URLs in GitHub READMEs with low strict precision, and the paper introduces a benchmark of 811 repositories and 1,439 URLs.

  3. MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)

    cs.CL 2025-09 unverdicted novelty 5.0 of 10

    Using LLM extraction on 681 papers, the authors build a public knowledge graph showing financial NLP moved from LLM adoption to limitation-aware, modular system design between 2022 and 2025.

  4. HyDRA: A Hybrid-Driven Reasoning Architecture for Verifiable Knowledge Graphs

    cs.LG 2025-07 conditional novelty 5.0 of 10

    HyDRA, a contract-driven LLM pipeline for building ontologies and knowledge graphs, scored 42-62% accuracy on MedExQA biomedical QA while an ontology-free baseline scored 95-98%.

  5. Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph

    cs.CL 2024-11 reject novelty 5.0 of 10

    WTS couples retrieval-augmented generation with an LLM-built, evolving domain knowledge graph and reports SOTA gains, but its main experiments use test-set gold answers to construct the graph.

  6. Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT

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    In a zero-shot Chinese relation extraction test on DuIE 2.0, gpt-4-turbo achieved the best F1 at 0.367, Gemini flash models were fastest, and LLaMA models performed worst.

  7. Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation

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    OntoKGen automates ontology extraction and knowledge graph generation from technical documents using LLMs with user-guided iterative prompting, demonstrated on a semiconductor equipment reliability case study.

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