Knowledge Graph Generator

DOI

Code and experiment results for a synthetic knowledge graph generator. The generator receives a set of rules, with an expected body support and support, and returns a knowledge graph that approximately matches the rules according to the body support and confidence.

This code was developed during the Bachelor thesis by Gabriel Glaser, Generating Random Knowledge Graphs from Rules, University of Stuttgart, 2024. Handle 11682/15486.

Identifier
DOI https://doi.org/10.18419/darus-4436
Related Identifier Cites https://doi.org/10.1145/3447772
Related Identifier Cites https://doi.org/10.1145/2488388.2488425
Related Identifier Cites https://doi.org/10.48550/arXiv.2307.06698
Related Identifier Cites https://doi.org/10.2172/1339361
Related Identifier Cites https://doi.org/10.1145/3543507.3583358
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-4436
Provenance
Creator Glaser, Gabriel Timon ORCID logo
Publisher DaRUS
Contributor Hernández, Daniel
Publication Year 2025
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Hernández, Daniel (Universität Stuttgart)
Representation
Resource Type Code files and JSON; Dataset
Format application/zip; text/plain; charset=US-ASCII; text/markdown
Size 397785961; 135768; 1111; 4917
Version 1.0
Discipline Other