Code for Faithful Embeddings for EL++ Knowledge Bases

DOI

This is the official pytorch implementation of the paper "Faithful embeddings for EL++ Knowledge Bases" published in ISWC 2022. The code was implemented based on el-embeddings.

The code can be used to reproduce the experiments on subsumption reasoning. To execute the code, follow the instructions in the README.md file. For more info, please check the paper. Please have no hesitation to contact the authors for any inquiries.

Further information can be found in the README.md. The code of this repository is also on Github

Identifier
DOI https://doi.org/10.18419/darus-3989
Related Identifier IsCitedBy https://doi.org/10.1007/978-3-031-19433-7_2
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3989
Provenance
Creator Xiong, Bo ORCID logo; Potyka, Nico ORCID logo; Tran, Trung-Kien; Nayyeri, Mojtaba ORCID logo; Staab, Steffen ORCID logo
Publisher DaRUS
Contributor Xiong, Bo; Staab, Steffen
Publication Year 2024
Funding Reference European Commission info:eu-repo/grantAgreement/EC/H2020/860801
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Xiong, Bo (Universität Stuttgart); Staab, Steffen (Universität Stuttgart)
Representation
Resource Type Dataset
Format text/plain; application/octet-stream; text/x-python; image/png; text/markdown; application/x-ipynb+json
Size 112927; 30722803; 862123; 6032284; 1728240; 190; 26579; 26421; 7894; 9378; 8643; 528; 52; 43; 168995; 5864; 14973315; 323145; 2261126; 646724; 483648; 15111157; 752136; 5264952; 1504184; 39572636; 561860; 4476792; 563388; 1555; 3763; 44061; 39572656; 21567274
Version 1.0
Discipline Computer Science; Computer Science, Electrical and System Engineering; Engineering Sciences; Logic; Mathematics; Natural Sciences