---
_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'
...