mlsanityData to 7 Oct 2026

Knowledge Engineering using Large Language Models

arXiv 2310.00637 ↗First posted 1 Oct 2023cs.AI cs.CLcited by 9

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Abstract (arXiv)

Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 58 of its 130 references that are in the map. First posted on arXiv 1 Oct 2023.

Builds on

58 of 130 references.

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38 more
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 2; 2026: 1.

9 papers.

  1. 2024OntoChat: a Framework for Conversational Ontology Engineering using Language Modelscited by 14
  2. 2024Integrating Large Language Models and Knowledge Graphs for Extraction and Validation of Textual Test Datacited by 5
  3. 2024Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Modelscited by 4
  4. 2026Automatic Prompt Optimization for Dataset-Level Feature Discoverycited by 2
  5. 2025How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performancecited by 1
  6. 2024Evaluating Class Membership Relations in Knowledge Graphs using Large Language Modelscited by 0
  7. 2024Enriching Ontologies with Disjointness Axioms using Large Language Modelscited by 0
  8. 2024Adapter-based Approaches to Knowledge-enhanced Language Models -- A Surveycited by 0
  9. 2025Reasoning Capabilities and Invariability of Large Language Modelscited by 0