Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Knowledge graphs and natural language are linked through methods like knowledge graph-to-text generation [1] and the use of natural language for describing graph semantics [2]. Furthermore, research focuses on bridging the semantic gap between these two formats to improve reasoning and retrieval in AI systems {fact:1, fact:2, fact:4}.
Facts (5)
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Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceRecent methods to bridge the semantic gap between knowledge graphs and natural language, such as joint graph-text embeddings, prompt-based schema alignment, and co-training frameworks, often require extensive tuning and are task-specific, lacking robust generalization, according to Peng et al. (2024).
claimKnowledge graph-to-text is a method that generates natural language text from structured knowledge graphs by leveraging models to map graph data into coherent, informative sentences.
claimThe structured format of knowledge graphs often fails to capture the richness and flexibility of natural language, creating a semantic gap that leads to poor retrieval of relevant knowledge and ineffective reasoning by Large Language Models.
Construction of intelligent decision support systems through ... - Nature nature.com 1 fact
claimThe combination of knowledge graphs and retrieval-augmented generation has the potential to build decision support systems that leverage structured knowledge representations through flexible interactions and reasoning in natural language.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org 1 fact
perspectiveHertling and Paulheim argue that semantics in knowledge graphs are typically described using natural language (labels, comments, or descriptions), relations between concepts, or formal axioms.