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Relations (1)
related 0.50 — strongly supporting 5 facts
Large Language Models are utilized as core components in entity linking frameworks, such as ChatEL and LLMAEL, to enhance context and generate candidates as described in [1] and [2]. Furthermore, they serve as tools for performing entity linking during knowledge graph construction [3], though their integration can also lead to representational conflicts that impact linking consistency [4], and they rely on entity linking as a technique to feed structured data into their processing layers [5].
Facts (5)
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Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 4 facts
referenceChatEL, proposed by Ding Y. et al. (2024), is a three-step framework that leverages large language models for entity linking by generating candidate entities, enhancing contextual information, and incorporating a multiple-choice format.
claimThe fusion of large language models (LLMs) and knowledge graphs (KGs) encounters representational conflicts between the implicit statistical patterns of LLMs and the explicit symbolic structures of KGs, which disrupts entity linking consistency.
referenceXin et al. (2024) developed 'LLMAEL', a method using large language models as context augmenters for entity linking.
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.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
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.