concept

ontology construction

Facts (14)

Sources

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 10 facts

referenceResearch by Saeedizade & Blomqvist (2024) and Lippolis et al. (2025b) evaluated GPT-4's performance in ontology construction and confirmed that its outputs approach the quality of novice human modelers, validating the feasibility of intelligent ontology assistants.

referenceThe GraphRAG (Edge et al., 2024) and OntoRAG (Tiwari et al., 2025) studies established a foundation for data-driven ontology construction by generating instance-level graphs from raw text via open information extraction, followed by abstracting ontological concepts and relations through clustering and generalization.

claimThe bottom-up trajectory of ontology construction prioritizes automatic extraction, schema induction, and dynamic evolution, whereas the top-down trajectory emphasizes semantic modeling, logical consistency, and expert-guided alignment.

claimSemi-automated ontology construction pipelines have evolved to encompass the entire lifecycle, from Competency Question (CQ) formulation and validation to ontology instantiation, with human experts intervening only at critical checkpoints.

referenceNeOn-GPT (Fathallah et al., 2025) and LLMs4Life (Fathallah et al., 2024) utilize end-to-end, prompt-driven workflows that integrate ontology reuse and adaptive refinement to construct coherent ontological structures in complex scientific domains like the life sciences.

referenceLippolis et al. (2025b) proposed two prompting strategies for ontology construction: a 'memoryless' approach for modular construction and a reflective iterative method inspired by the Ontogenia framework.

claimTop-down research on LLM-assisted ontology construction emphasizes semantic consistency, structural completeness, and human–AI collaboration.

referenceThe top-down ontology construction paradigm prioritizes conceptual abstraction, the precise definition of relations, and structured semantic representation to ensure that subsequent knowledge extraction and instance population adhere to well-defined logical constraints.

referenceThe paper 'LLM-empowered knowledge graph construction: A survey' is organized into sections covering foundations of traditional KG construction (Section 2), LLM-enhanced ontology construction (Section 3), LLM-driven knowledge extraction (Section 4), LLM-powered knowledge fusion (Section 5), and future research directions including KG-based reasoning and dynamic knowledge memory (Section 6).

claimNatural language-based ontology construction aims to induce semantic schemas directly from unstructured or semi-structured text corpora, removing the need for explicitly formulated questions.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 2 facts

claimWorldKG and HKGB utilize semi-automatic methods to build initial ontologies, which is more advanced than manual ontology construction.

claimResearch by authors in citations [145, 146, 147] suggests that fully automatic ontology construction is likely not possible, leading to a focus on semi-automatic construction methods for single sources.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 1 fact

referencePrevious academic surveys have established a roadmap for unifying LLMs and KGs (Pan et al., 2024), discussed opportunities and challenges in leveraging LLMs for knowledge extraction and ontology construction (Pan et al., 2023), summarized integration paradigms (Kau et al., 2024; Ibrahim et al., 2024), and provided overviews of knowledge injection methods (Song et al., 2025), multilingual KG question answering (Perevalov et al., 2024), temporal KG QA (Su et al., 2024), complex QA (Daull et al., 2023), and the intersection of search engines, KGs, and LLMs for user information seeking (Hogan et al., 2025).

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

claimThe schema for a knowledge graph is defined as an ontology, which describes the properties of a specific domain and how they are related, making ontology construction an essential stage of knowledge graph construction.