mlsanityData to 7 Oct 2026

Conversational Ontology Alignment with ChatGPT

arXiv 2308.09217 ↗First posted 18 Aug 2023cs.CLcited by 11

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Abstract (arXiv)

This study evaluates the applicability and efficiency of ChatGPT for ontology alignment using a naive approach. ChatGPT's output is compared to the results of the Ontology Alignment Evaluation Initiative 2022 campaign using conference track ontologies. This comparison is intended to provide insights into the capabilities of a conversational large language model when used in a naive way for ontology matching, and to investigate the potential advantages and disadvantages of this approach.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 18 Aug 2023.

Builds on

4 of 11 references.

References · 4

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  3. 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4cited by 202
  4. 2018Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classescited by 1

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Citing papers per year: 2023: 1; 2024: 5; 2025: 5.

11 papers.

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  1. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
  2. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
  3. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
  4. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4
  5. 2024Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study
  6. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignment
  7. 2023Consistency Analysis of ChatGPT
  8. 2024ChatGPT Alternative Solutions: Large Language Models Survey