Mapping of ImageNet and Wikidata for Knowledge Graphs Enabled Computer Vision
Keywords:ImageNet, Wikidata, mapping, computer vision, knowledge graphs
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.
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives. Dbpedia: A nucleus for a web of open data. In The semantic web, pages 722–735. Springer, 2007.
L. Beyer, O. J. H´ enaff, A. Kolesnikov, X. Zhai, and A. v. d. Oord. Are we done with imagenet? arXiv preprint arXiv:2006.07159, 2020.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26:2787–2795, 2013.
R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin. Knowledge graph transfer network for few-shot recognition. In AAAI, pages 10575–10582, 2020.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
C. Edwards. Linking knowledge graphs and images using embeddings. https://cnedwards.com/files/studyabroad_report.pdf, 2018.
L. Foppiano and L. Romary. entity-fishing: a dariah entity recognition and disambiguation service. Journal of the Japanese Association for Digital Humanities, 5(1):22–60, 2020.
M. Huh, P. Agrawal, and A. A. Efros. What makes imagenet good for transfer learning? arXiv preprint arXiv:1608.08614, 2016.
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90, 2017.
A. Lerer, L. Wu, J. Shen, T. Lacroix, L. Wehrstedt, A. Bose, and A. Peysakhovich. PyTorch-BigGraph: A Large-scale Graph Embedding System. In Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA, 2019.
G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41, 1995.
F. A° . Nielsen. Linking imagenet wordnet synsets with wikidata. In Companion Proceedings of the The Web Conference 2018, pages 1809–1814, 2018.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252, 2015.
I. Stavrakantonakis, A. Fensel, and D. Fensel. Matching web entities with potential actions. In SEMANTICS (Posters & Demos), pages 35–38. Citeseer, 2014.
D. Vrandeˇci´c and M. Kr¨otzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78–85, 2014.
W. Wang, V. W. Zheng, H. Yu, and C. Miao. A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–37, 2019.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (CSUR), 53(3):1–34, 2020.
H.-M. Yang, X.-Y. Zhang, F. Yin, and C.-L. Liu. Robust classification with convolutional prototype learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3474–3482, 2018.
K. Yang, K. Qinami, L. Fei-Fei, J. Deng, and O. Russakovsky. Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 547–558, 2020.
A. V. Zhdanova and P. Shvaiko. Community-driven ontology matching. In European Semantic Web Conference, pages 34–49. Springer, 2006.
Copyright (c) 2021 Dominik Filipiak, Anna Fensel, Agata Filipowska
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