Knowledge Tree
Relations (1)
related 4.09 — strongly supporting 16 facts
Knowledge graphs and language models are deeply interconnected through research frameworks like KGGen [1], QA-GNN [2], and JAKET [3], which facilitate bidirectional enhancement and joint reasoning. Furthermore, language models are frequently utilized to extract structured triples from text to populate knowledge graphs {fact:11, fact:15}, while knowledge graphs are used to provide structured context to enhance the performance of language models {fact:10, fact:14}.
Facts (16)
Sources
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 8 facts
referenceReLMKG, proposed by Cao and Liu in 2023, uses a language model to encode complex questions and guides a graph neural network in message propagation and aggregation through outputs from different layers.
claimApproaches like K-BERT and BERT-MK face limitations including potential latency and conflicts when integrating knowledge graphs with language models.
referenceShen et al. (2022) optimize semantic representations from language models and structural knowledge in knowledge graphs through a probabilistic loss.
referenceZhang M. et al. (2024) proposed an LLM-enhanced embedding framework for knowledge graph error validation that uses graph structure information to identify suspicious triplet relations and then uses a language model for validation.
referenceJAKET (Yu et al., 2022) enables bidirectional enhancement between knowledge graphs and language models.
claimPre-trained transformer-based methods, such as the model by Lukovnikov et al. (2019) and ReLMKG (Cao and Liu, 2023), use language models to bridge semantic gaps between questions and knowledge graph structures.
referenceHao et al. (2022) introduced 'Bertnet', a system for harvesting knowledge graphs with arbitrary relations from pre-trained language models.
referenceSun et al. (2021a) proposed 'Jointlk', a method for joint reasoning with language models and knowledge graphs for commonsense question answering.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 2 facts
procedureThe storage process for Knowledge Graphs involves converting unstructured text data into a structured Knowledge Graph by extracting triples using a language model (LM_ext).
procedureThe storage process for Knowledge Graphs involves converting unstructured text data into a structured Knowledge Graph by extracting triples using a language model (LM_ext).
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org 1 fact
referenceYasunaga et al. introduced QA-GNN, a method for reasoning with language models and knowledge graphs for question answering, in an arXiv preprint in 2021.
Unknown source 1 fact
claimKnowledge-graph-enhanced Large Language Models (KG-enhanced LLMs) merge the strengths of structured knowledge graphs and unstructured language models to enable AI systems to achieve higher capabilities.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
claimLanguage models can extract triples from unstructured texts to enrich knowledge graphs with new knowledge that can be added to the graph structure.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 1 fact
referenceKnowledge integration and fusion enhance language models by aligning knowledge graphs and text via local subgraph extraction and entity linking, then feeding the aligned data into a cross-model encoder to bidirectionally fuse text and knowledge graphs for joint training.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org 1 fact
referenceBelinda Mo, Kyssen Yu, Joshua Kazdan, Proud Mpala, Lisa Yu, Chris Cundy, Charilaos Kanatsoulis, and Sanmi Koyejo authored the paper 'KGGen: Extracting Knowledge Graphs from Plain Text with Language Models.'
Construction of intelligent decision support systems through ... - Nature nature.com 1 fact
claimThe IKEDS framework, designed for cross-domain decision support on complex tasks, integrates knowledge graphs with retrieval-augmented generation (RAG) by combining neural and symbolic AI to enhance language models with structured knowledge.