entities ↔ Large Language Models

Relations (1)

related 0.50 — strongly supporting 5 facts

Large Language Models are related to entities because they are used to extract, encode, and interpret entities within knowledge graphs as described in [1], [2], and [3], while also utilizing them to improve output consistency via RAG [4] and assisting in database schema design [5].

Facts (5)

Sources

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

claimLLM-augmented KG approaches utilize the generalization capabilities of LLMs to perform tasks such as enriching graph representations, performing knowledge completion (generating new facts), and extracting entities and relationships from text to construct new graphs.

claimLarge Language Models (LLMs) can assist in database schema design by suggesting relationships and entities based on provided data, which improves the efficiency of database management systems.

claimModels such as KEPLER and Pretrain-KGE use BERT-like LLMs to encode textual descriptions of entities and relationships into vector representations, which are then fine-tuned on KG-related tasks.

claimIntegrating knowledge graphs with large language models via Retrieval-augmented generation (RAG) allows the retriever to fetch relevant entities and relations from the knowledge graph, which enhances the interpretability and factual consistency of the large language model's outputs.

KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org arXiv 1 fact

claimGraph-structured data captures relationships between entities and provides structural information, which enables Large Language Models (LLMs) to interpret external knowledge more effectively.