Show simple item record

dc.contributor.authorAli, Mehdi
dc.contributor.authorJabeen, Hajira
dc.contributor.authorHoyt, Charles Tapley
dc.contributor.authorLehman, Jens
dc.date.accessioned2020-02-26T07:50:30Z
dc.date.available2020-02-26T07:50:30Z
dc.date.issued2020-01-28
dc.identifier.urihttp://hdl.handle.net/10754/661705
dc.description.abstractThere is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the similarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.
dc.description.sponsorshipThis work was partly supported by the KAUST project grant Bio2Vec (grant no. 3454), the European Unions Horizon 2020 funded project BigDataOcean (GA no. 732310), the CLEOPATRA project (GA no. 812997), and the German national funded BmBF project MLwin.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2001.10560
dc.rightsArchived with thanks to arXiv
dc.titleThe KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionSmart Data Analytics Group, University of Bonn, Germany
dc.contributor.institutionDepartment of Enterprise Information Systems, Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Germany
dc.contributor.institutionDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
dc.identifier.arxividarXiv:2001.10560
refterms.dateFOA2020-02-26T07:50:59Z


Files in this item

Thumbnail
Name:
Preprintfile1.pdf
Size:
701.7Kb
Format:
PDF
Description:
Pre-print

This item appears in the following Collection(s)

Show simple item record