Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction

• URL: http://arxiv.org/abs/2404.03868v2
• Date: Wed, 02 Oct 2024 05:51:53 GMT
• Title: Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
• Authors: Bowen Zhang, Harold Soh,
• Abstract summary: We propose a three-phase framework named Extract-Define-Canonicalize (EDC) EDC is flexible in that it can be applied to settings where a pre-defined target schema is available and when it is not. We demonstrate EDC is able to extract high-quality triplets without any parameter tuning and with significantly larger schemas compared to prior works.
• Score: 12.455647753787442
• License:
• Abstract: In this work, we are interested in automated methods for knowledge graph creation (KGC) from input text. Progress on large language models (LLMs) has prompted a series of recent works applying them to KGC, e.g., via zero/few-shot prompting. Despite successes on small domain-specific datasets, these models face difficulties scaling up to text common in many real-world applications. A principal issue is that, in prior methods, the KG schema has to be included in the LLM prompt to generate valid triplets; larger and more complex schemas easily exceed the LLMs' context window length. Furthermore, there are scenarios where a fixed pre-defined schema is not available and we would like the method to construct a high-quality KG with a succinct self-generated schema. To address these problems, we propose a three-phase framework named Extract-Define-Canonicalize (EDC): open information extraction followed by schema definition and post-hoc canonicalization. EDC is flexible in that it can be applied to settings where a pre-defined target schema is available and when it is not; in the latter case, it constructs a schema automatically and applies self-canonicalization. To further improve performance, we introduce a trained component that retrieves schema elements relevant to the input text; this improves the LLMs' extraction performance in a retrieval-augmented generation-like manner. We demonstrate on three KGC benchmarks that EDC is able to extract high-quality triplets without any parameter tuning and with significantly larger schemas compared to prior works. Code for EDC is available at https://github.com/clear-nus/edc.

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