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Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 3 facts

referenceAl-Aswadi, Chan, and Gan published 'Automatic ontology construction from text: a review from shallow to deep learning trend' in Artificial Intelligence Review in 2020.

referenceWong, Liu, and Bennamoun published 'Ontology learning from text: A look back and into the future' in ACM Computing Surveys in 2012.

referenceBrowarnik and Maimon published 'Ontology learning from text: why the ontology learning layer cake is not viable' in the International Journal of Signs and Semiotic Systems in 2015.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 2 facts

claimSemantic layers serve as a bridge between LLMs and KGs by mapping raw data into interpretable forms, which enhances the model's ability to understand and generate text, improves output accuracy, and increases contextual relevance.

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].

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 2 facts

claimImages and text are the most common input data types used in current neuro-symbolic methods.

claimMost neuro-symbolic AI research currently utilizes unimodal and non-heterogeneous representation spaces, focusing on single data types such as text, images, or structured data.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 1 fact

claimMultimodal Knowledge Graph construction aims to integrate heterogeneous modalities, including text, images, audio, and video, into unified, structured representations to enable richer reasoning and cross-modal alignment.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 1 fact

claimThe construction of multi-modal knowledge graphs is complicated and inefficient because it requires the exploration of entities across different modalities, such as texts and images.

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer Dec 9, 2025 1 fact

claimNeural components in learning for reasoning systems act as heuristic guides or translators that convert unstructured modalities, such as images and text, into symbolic formats, making them amenable to deductive reasoning engines.