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Generative Knowledge Graph Construction: A Review

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arxiv 2210.12714 v3 pith:OQ7SNM6O submitted 2022-10-23 cs.CL cs.AIcs.DBcs.IRcs.LG

Hongbin Ye , Ningyu Zhang , Hui Chen , Huajun Chen This is my paper
classification cs.CLcs.AIcs.DBcs.IRcs.LG
keywords generativeknowledgeconstructiongraphmethodsanalysisdirectionsempirical
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Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.

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  1. Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Cascading cheap and expensive language models, with answers validated against regex-extracted candidate dates, improved speed and modestly improved accuracy when extracting dates of birth from medical documents.

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