[{"language":[{"iso":"eng"}],"_id":"31806","user_id":"477","doi":"10.1145/3511095.3531287","ddc":["000"],"author":[{"id":"78256","last_name":"Dreßler","first_name":"Kevin","full_name":"Dreßler, Kevin"},{"id":"67234","first_name":"Mohamed","last_name":"Sherif","full_name":"Sherif, Mohamed"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","id":"65716"}],"conference":{"start_date":"2022-06-28","name":"HT ’22: 33rd ACM Conference on Hypertext and Social Media","location":"Barcelona (Spain)","end_date":"2022-07-01"},"status":"public","title":"ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning","year":"2022","date_updated":"2022-11-18T10:11:38Z","date_created":"2022-06-08T08:47:33Z","department":[{"_id":"34"}],"keyword":["2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba"],"type":"conference","citation":{"mla":"Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” <i>Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia</i>, 2022, doi:<a href=\"https://doi.org/10.1145/3511095.3531287\">10.1145/3511095.3531287</a>.","bibtex":"@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={<a href=\"https://doi.org/10.1145/3511095.3531287\">10.1145/3511095.3531287</a>}, booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }","ama":"Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. In: <i>Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia</i>. ; 2022. doi:<a href=\"https://doi.org/10.1145/3511095.3531287\">10.1145/3511095.3531287</a>","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: <a href=\"https://doi.org/10.1145/3511095.3531287\">10.1145/3511095.3531287</a>.","apa":"Dreßler, K., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. <i>Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia</i>. HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain). <a href=\"https://doi.org/10.1145/3511095.3531287\">https://doi.org/10.1145/3511095.3531287</a>","short":"K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.","chicago":"Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” In <i>Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia</i>, 2022. <a href=\"https://doi.org/10.1145/3511095.3531287\">https://doi.org/10.1145/3511095.3531287</a>."},"publication":"Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"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"}]}]