Knowledge Tree
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related 3.00 — strongly supporting 7 facts
Knowledge graphs serve as a structured foundation that enables reasoning capabilities in AI systems, as evidenced by their role in neurosymbolic techniques [1], complex question answering [2], and explainable AI [3]. They are explicitly defined as infrastructure for structured reasoning [4] and are integrated with LLMs to enhance reasoning performance {fact:5, fact:6}, while also allowing for the derivation of new knowledge through logical inference [5].
Facts (7)
Sources
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
referenceCao and Liu (2023) proposed RELMKG, a method for reasoning with pre-trained language models and knowledge graphs for complex question answering, published in Applied Intelligence.
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org 1 fact
claimIntegrating Knowledge Graphs (KGs) with Retrieval-Augmented Generation (RAG) enhances the knowledge representation and reasoning abilities of Large Language Models (LLMs) by utilizing structured knowledge, which enables the generation of more accurate answers.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org 1 fact
referenceLauren Nicole DeLong, Ramon Fernández Mir, and Jacques D Fleuriot conducted a survey on neurosymbolic AI techniques for reasoning over knowledge graphs.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org 1 fact
claimKnowledge graphs can derive new knowledge through reasoning and describe real-world entities from open knowledge bases (such as DBpedia, schema.org, or YAGO) or organization-specific entities.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
claimIn a synergized framework, Large Language Models use structured knowledge from Knowledge Graphs to improve reasoning and understanding, while Knowledge Graphs utilize the language production and contextual capabilities of Large Language Models.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 1 fact
referenceXplainLLM (Chen et al., 2024d) is a question-answering dataset for Large Language Models and Knowledge Graphs that focuses on question-answering explainability and reasoning.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org 1 fact
claimKnowledge Graphs serve as a fundamental infrastructure for structured knowledge representation and reasoning, providing a unified semantic foundation for applications such as semantic search, question answering, and scientific discovery.