--- _id: '31806' abstract: - lang: eng text: The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds author: - first_name: Kevin full_name: Dreßler, Kevin id: '78256' last_name: Dreßler - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. In: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. ; 2022. doi:10.1145/3511095.3531287' apa: 'Dreßler, K., Sherif, M., & Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain). https://doi.org/10.1145/3511095.3531287' bibtex: '@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={10.1145/3511095.3531287}, booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }' chicago: Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” In Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022. https://doi.org/10.1145/3511095.3531287. ieee: 'K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,” presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi: 10.1145/3511095.3531287.' mla: Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287. short: 'K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.' conference: end_date: 2022-07-01 location: Barcelona (Spain) name: 'HT ’22: 33rd ACM Conference on Hypertext and Social Media' start_date: 2022-06-28 date_created: 2022-06-08T08:47:33Z date_updated: 2022-11-18T10:11:38Z ddc: - '000' department: - _id: '34' doi: 10.1145/3511095.3531287 keyword: - 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia status: public title: ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning type: conference user_id: '477' year: '2022' ...