Knowledge Tree
natural language
Also known as: natural language text, natural languages
Facts (21)
Sources
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 5 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.
claimA critical challenge in Large Language Model-powered Knowledge Graph Question Answering (KGQA) systems is semantic drift during the conversion of natural language to knowledge graph queries, as noted by Li H. et al. (2024).
referenceBrown et al. (1992) developed class-based n-gram models of natural language, published in Computational Linguistics.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 3 facts
claimHellaSwag is a benchmark for evaluating commonsense reasoning in natural language by testing a model's ability to complete sentences coherently and sensibly.
referenceIn LLM-augmented Knowledge Graphs, LLMs are used to improve KG representations, encode text or generate facts for KG completion, perform entity discovery and relation extraction for KG construction, describe KG facts in natural language, and connect natural language questions to KG-based answers, as cited in [55, 56, 57].
referenceElsahar H, Vougiouklis P, Remaci A, Gravier C, Hare J, Laforest F, and Simperl E published 'T-rex: A large scale alignment of natural language with knowledge base triples' in the Proceedings of LREC in 2018.
Building Better Agentic Systems with Neuro-Symbolic AI cutter.com Dec 10, 2025 2 facts
claimNeuro-symbolic AI improves explainability in lending agents by using a neural network to analyze unstructured data like emails and business plans, while a symbolic component makes the final decision based on regulatory rules, producing a clear, transparent audit trail in natural language.
claimNeural networks demonstrate fluency by analyzing and generating human-like natural language input and output.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 2 facts
claimEntity disambiguation is a primary challenge in knowledge graph construction because the same entity may have various expressions across different knowledge graphs due to the polysemy problem in natural language.
referenceMinervini P, Bošnjak M, Rocktäschel T et al. published 'Differentiable reasoning on large knowledge bases and natural language' in the proceedings of the AAAI Conference on Artificial Intelligence in 2020.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org Jan 7, 2025 2 facts
claimLLM-based approaches do not incorporate a definitive measure of relatedness between two elements, instead delineating the relationship in natural language.
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.
Construction of intelligent decision support systems through ... - Nature nature.com Oct 10, 2025 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.
Quantum Approaches to Consciousness plato.stanford.edu Nov 30, 2004 1 fact
referenceGabora and Aerts (2002, 2009) explored meaning in natural languages using semantic networks, describing the contextual manner in which concepts are evoked, used, and combined, including concept association in an evolutionary context.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 1 fact
procedureThe neural symbolic framework proposed by Kimura et al. (2021) for text-based games follows a multi-step process: (1) a semantic parser extracts basic propositional logic from text observations in the environment, converting natural language into logical expressions; (2) external knowledge bases like ConceptNet are used to understand word semantic categories and refine the extracted propositional logic; (3) the refined logic and lexical category information are combined via a First Order Logic (FOL) converter into logical facts representing game state conditions; (4) these logical facts are used as training input for a Logical Neural Network (LNN).
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 1 fact
claimRule-based methods like DBpedia extracted triples from Wikipedia infoboxes using predefined rules, which provided efficiency for fixed-format data but struggled with the complex semantics of natural language.
LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph puppygraph.com Feb 19, 2026 1 fact
claimGraphRAG systems abstract traditional database interactions, allowing users to query systems using natural language instead of specialized query languages like Cypher, Gremlin, or SPARQL.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 1 fact
claimLarge Language Models (LLMs) are probabilistic models of natural language that autoregressively estimate the likelihood of word sequences by analyzing text data.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 1 fact
claimGary Lupyan's research focuses on how natural language scaffolds and augments human cognition, the evolution of language, and how language adapts to the needs of its learners and users.