Mapping of ImageNet and Wikidata for Knowledge Graphs Enabled Computer Vision

Dominik Filipiak, Anna Fensel, Agata Filipowska

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

1 Citation (Scopus)

Abstract

Knowledge graphs are used as a source of prior knowledge in numerous computer vision tasks. However, such an approach requires to have a mapping between ground truth data labels and the target knowledge graph. We linked the ILSVRC 2012 dataset (often simply referred to as ImageNet) labels to Wikidata entities. This enables using rich knowledge graph structure and contextual information for several computer vision tasks, traditionally benchmarked with ImageNet and its variations. For instance, in few-shot learning classification scenarios with neural networks, this mapping can be leveraged for weight initialisation, which can improve the final performance metrics value. We mapped all 1000 ImageNet labels – 461 were already directly linked with the exact match property (P2888), 467 have exact match candidates, and 72 cannot be matched directly. For these 72 labels, we discuss different problem categories stemming from the inability of finding an exact match. Semantically close non-exact match candidates are presented as well. The mapping is publicly available athttps://github.com/DominikFilipiak/imagenet-to-wikidata-mapping.
Original languageEnglish
Title of host publication2021: 24th International Conference on Business Information Systems: 15-17 June 2021 Hannover, Germany "Enterprise Knowledge and Data Spaces"
Subtitle of host publicationConference proceedings
EditorsW. Abramowicz, S. Auer
Pages151-161
DOIs
Publication statusPublished - 2021
Externally publishedYes

Publication series

NameBusiness Information Systems
Number1/2021
ISSN (Electronic)2747-9986

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