{"doi":"10.1145/3511095.3531287","abstract":[{"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","lang":"eng"}],"ddc":["000"],"citation":{"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.","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.","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","short":"K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.","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","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."},"user_id":"477","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"department":[{"_id":"34"}],"keyword":["2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba"],"publication":"Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia","date_created":"2022-06-08T08:47:33Z","title":"ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning","type":"conference","language":[{"iso":"eng"}],"_id":"31806","conference":{"end_date":"2022-07-01","name":"HT ’22: 33rd ACM Conference on Hypertext and Social Media","start_date":"2022-06-28","location":"Barcelona (Spain)"},"year":"2022","date_updated":"2022-11-18T10:11:38Z","author":[{"full_name":"Dreßler, Kevin","last_name":"Dreßler","first_name":"Kevin","id":"78256"},{"full_name":"Sherif, Mohamed","last_name":"Sherif","first_name":"Mohamed","id":"67234"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","first_name":"Axel-Cyrille"}],"status":"public"}