relation extraction ↔ Large Language Models

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related 13.00 — strongly supporting 13 facts

Large Language Models are extensively utilized as a core technology for performing relation extraction in tasks such as Knowledge Graph construction, semantic pattern induction, and entity discovery, as evidenced by [1], [2], [3], and [4]. Furthermore, research explores advanced methodologies like prompt engineering and selective attention mechanisms to enhance the precision of relation extraction using these models, as noted in [5], [6], and [7].

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A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer 4 facts

procedureThe process of integrating KGs with LLMs begins with data preparation, which involves extracting entities and relationships from KGs using techniques like Named Entity Recognition (NER) and relation extraction.

procedureThe LLM-augmented KG process is structured into two principal stages: (1) synthesizing KGs by applying LLMs to perform coreference resolution, named entity recognition, and relationship extraction to relate entities from input documents; (2) performing tasks on the constructed KG using LLMs, including KG completion to fill gaps, KG question answering to query responses, and KG text generation to develop descriptions of nodes.

referenceIn LLM-augmented Knowledge Graphs, LLMs are used to improve KG representations, encode text or generate facts for KG completion, perform entity discovery and relation extraction for KG construction, describe KG facts in natural language, and connect natural language questions to KG-based answers, as cited in [55, 56, 57].

procedureSemantic parsing, entity linking, and relation extraction are techniques used to implement semantic layers by extracting and inferring critical concepts and relationships from data to feed into LLMs during processing.

Combining large language models with enterprise knowledge graphs frontiersin.org Frontiers 2 facts

claimThe authors of 'Combining large language models with enterprise knowledge graphs' identify LLMs, knowledge graph, relation extraction, knowledge graph enrichment, AI, enterprise AI, carbon footprint, and human in the loop as the primary keywords for their research.

referenceRecent literature identifies two primary approaches to named entity recognition and relation extraction: creating large training sets with hand-curated or extensive automatic annotations to fine-tune large language models, or using precise natural language instructions to replace domain knowledge with prompt engineering.

A Survey of Incorporating Psychological Theories in LLMs - arXiv arxiv.org arXiv 2 facts

claimMaharaj et al. (2023) and Yu et al. (2022) leverage selective attention mechanisms in LLMs to detect hallucinations and extract relations.

referenceXin Miao, Yongqi Li, Shen Zhou, and Tieyun Qian proposed a neuromorphic mechanism for episodic memory retrieval in large language models to generate commonsense counterfactuals for relation extraction, as detailed in their 2024 paper in the Findings of the Association for Computational Linguistics: ACL 2024.

LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org arXiv 1 fact

claimThe relation extraction component utilizes Large Language Models (LLMs) with advanced prompt engineering, incorporating both contextual data from the Contextual Retrieval Module (CRM) and extracted entities as input to enhance the precision and relevance of relationship extraction.

Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org arXiv 1 fact

claimUtilizing LLMs for tasks like relation extraction and property identification in the KG construction process can make the construction more automatic while maintaining accuracy.

Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv 1 fact

claimBuilding a knowledge graph at enterprise scale incurs significant GPU or CPU costs and high latency when relying on Large Language Models or heavyweight NLP pipelines for entity and relation extraction.

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 1 fact

claimLarge Language Models (LLMs) assist in Knowledge Graph construction by acting as prompts and generators for entity, relation, and event extraction, as well as performing entity linking and coreference resolution.

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

referenceThe LLMs4OL framework, developed by Giglou et al. (2023), verified the capacity of Large Language Models for concept identification, relation extraction, and semantic pattern induction in general-purpose domains.