Replication Data for: AI-Assisted RDF Authoring from JSON Data

Version 1.0
Neubauer, Felix, 2026, "Replication Data for: AI-Assisted RDF Authoring from JSON Data", https://doi.org/10.18419/DARUS-6299, DaRUS, V1, UNF:6:j3H34z5Ux4jb6/he8Hprww== [fileUNF]
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Description

This dataset accompanies the paper AI-Assisted RDF Authoring from JSON Data. It contains the metal-organic framework (MOF) synthesis JSON input used in the paper, the natural-language hints submitted to MetaConfigurator's AI assistant, the RML mappings generated from those hints, the RDF (JSON-LD) results obtained by executing the mappings, the SHACL shape used to validate the successful result, and the prompt, generated query, and CSV result of the AI-assisted SPARQL workflow.

The artifacts document three RML-generation runs and one SPARQL run: a minimal hint with gpt-4o (valid but shallow mapping), the same detailed hint with gpt-4o (retained as a failure case: unconditional parent-map links cause Cartesian products and drop required relations) and with gpt-5-mini (the successful case used in the paper), and a natural-language SPARQL request answered by gpt-5-mini. The prompts contain natural-language modeling instructions but no worked RML/Turtle snippets.

The deposited files are the exact artifacts evaluated in the paper. Model outputs can vary across invocations and service versions. See README.md for the full file inventory and reproduction instructions (including pySHACL validation).

(2026-07-07)
Subject Chemistry; Computer and Information Science
Keyword Data Model, JSON Schema, Research Data Management, Resource Description Framework, JSON-LD, RDF Mapping Language, SPARQL, SHACL, Knowledge Graph, Linked Data, Semantic Web, Ontology, Metal-Organic Framework, Large Language Model, MetaConfigurator, Semantic Web, RDF (Document markup language), MOFs
Related Publication Is Supplement To: Neubauer, Felix, et al. (2026). MetaConfigurator: AI-Assisted RDF Authoring from JSON Data. arXiv Preprint.doi: 10.48550/arXiv.2606.07094
Notes The `rml:source` in the mappings is `Data.json` (the application's internal source name); rename `mof_synthesis_input.json` accordingly when running a mapping in another RML processor. LLM outputs are non-deterministic; the deposited mappings, RDF, query, and CSV are the exact artifacts from the paper.
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detailed_hint.txt
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MD5: ef1f052d8cfa4ad537f5900c95da7c08
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detailed_hint_gpt4_failed_rdf_result.json
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Published Jul 10, 2026
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MD5: 99e780dc5d934240415367072aefd107
detailed_hint_gpt4_failed_rml_mapping.ttl
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detailed_hint_gpt5_mini_success_rdf_result.json
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Published Jul 10, 2026
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detailed_hint_gpt5_mini_success_rml_mapping.ttl
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MD5: a97dc13b39733dbb3e49b61cc3e505bd
minimal_hint.txt
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Published Jul 10, 2026
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minimal_hint_gpt4_rdf_result.json
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MD5: 3cb93085c20a10a3a8aaa93fcefc3a5e
minimal_hint_gpt4_rml_mapping.ttl
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mof_synthesis_input.json
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Published Jul 10, 2026
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MD5: c675cc724a49973560b2c19b07497a1f
README.md
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Published Jul 10, 2026
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MD5: 907c0f808274ac94847e926c3f476693
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Curation Status History
StatusDateAssigner
2026-07-10 11:25:54Felix Neubauer
In Review by FoKUS2026-07-10 08:29:00Florian Fritze
Waiting for Feedback from Author2026-07-10 05:50:59Florian Fritze
In Review by FoKUS2026-07-07 13:06:02Dataverse Admin
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