Conversational Ontology Alignment with ChatGPT

Mohammad Saeid Mahdavinejad; Pascal Hitzler; Sanaz Saki Norouzi

Conversational Ontology Alignment with ChatGPT

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2308.09217 v1 pith:VNZA7EH3 submitted 2023-08-18 cs.CL

Sanaz Saki Norouzi , Mohammad Saeid Mahdavinejad , Pascal Hitzler This is my paper · ORCID

classification cs.CL

keywords ontologyalignmentchatgptapproachconversationalnaiveadvantagesapplicability

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Ontology Matching with Large Language Models and Prioritized Depth-First Search
cs.IR 2025-01 conditional novelty 5.0 of 10

A retrieve-identify-prompt pipeline plus prioritized depth-first search achieves state-of-the-art F-Measure on most OAEI 2024 tasks while sending only uncertain matches to an LLM.

Pith tools