knowledge extraction ↔ Large Language Models

Relations (1)

related 2.00 — strongly supporting 3 facts

Large Language Models are related to knowledge extraction because LLMs transform Knowledge Graph construction paradigms to include knowledge extraction [1], facilitate knowledge extraction from unstructured texts through integration with KGs [2], and are leveraged for knowledge extraction in academic surveys [3].

Facts (3)

Sources

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer 1 fact

claimThe integration of LLMs and KGs facilitates knowledge extraction and enrichment because LLMs can identify relevant information from unstructured texts to update KGs, while KGs provide a continuously updated and comprehensive knowledge base for LLMs.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv 1 fact

referencePrevious academic surveys have established a roadmap for unifying LLMs and KGs (Pan et al., 2024), discussed opportunities and challenges in leveraging LLMs for knowledge extraction and ontology construction (Pan et al., 2023), summarized integration paradigms (Kau et al., 2024; Ibrahim et al., 2024), and provided overviews of knowledge injection methods (Song et al., 2025), multilingual KG question answering (Perevalov et al., 2024), temporal KG QA (Su et al., 2024), complex QA (Daull et al., 2023), and the intersection of search engines, KGs, and LLMs for user information seeking (Hogan et al., 2025).

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv 1 fact

claimLarge Language Models are transforming Knowledge Graph construction by shifting the paradigm from rule-based and modular pipelines toward unified, adaptive, and generative frameworks across ontology engineering, knowledge extraction, and knowledge fusion.