---
_id: '63572'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Moshood Olawale
full_name: Yekini, Moshood Olawale
id: '114533'
last_name: Yekini
- first_name: Michael
full_name: Röder, Michael
id: '67199'
last_name: Röder
orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Yasir
full_name: Mahmood, Yasir
id: '99353'
last_name: Mahmood
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Demir C, Yekini MO, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class
Expression Learner over Large Graphs. In: Lecture Notes in Computer Science.
Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-06066-2_29'
apa: Demir, C., Yekini, M. O., Röder, M., Mahmood, Y., & Ngonga Ngomo, A.-C.
(2025). Tree-Based OWL Class Expression Learner over Large Graphs. Lecture
Notes in Computer Science. European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases, Porto. https://doi.org/10.1007/978-3-032-06066-2_29
bibtex: '@inproceedings{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham},
title={Tree-Based OWL Class Expression Learner over Large Graphs}, DOI={10.1007/978-3-032-06066-2_29},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Demir, Caglar and Yekini, Moshood Olawale and Röder, Michael and Mahmood,
Yasir and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Demir, Caglar, Moshood Olawale Yekini, Michael Röder, Yasir Mahmood, and
Axel-Cyrille Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large
Graphs.” In Lecture Notes in Computer Science. Cham: Springer Nature Switzerland,
2025. https://doi.org/10.1007/978-3-032-06066-2_29.'
ieee: 'C. Demir, M. O. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based
OWL Class Expression Learner over Large Graphs,” presented at the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases,
Porto, 2025, doi: 10.1007/978-3-032-06066-2_29.'
mla: Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.”
Lecture Notes in Computer Science, Springer Nature Switzerland, 2025, doi:10.1007/978-3-032-06066-2_29.
short: 'C. Demir, M.O. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.'
conference:
end_date: 2025-09-19
location: Porto
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2025-09-15
date_created: 2026-01-12T17:13:22Z
date_updated: 2026-01-12T17:17:07Z
department:
- _id: '574'
- _id: '923'
doi: 10.1007/978-3-032-06066-2_29
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783032060655'
- '9783032060662'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Tree-Based OWL Class Expression Learner over Large Graphs
type: conference
user_id: '67199'
year: '2025'
...
---
_id: '63575'
author:
- first_name: Sourabh
full_name: Kapoor, Sourabh
last_name: Kapoor
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Michael
full_name: Röder, Michael
id: '67199'
last_name: Röder
orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Kapoor S, Sharma A, Röder M, Demir C, Ngonga Ngomo A-C. Robustness Evaluation
of Knowledge Graph Embedding Models Under Non-targeted Attacks. In: Lecture
Notes in Computer Science. Springer Nature Switzerland; 2025. doi:10.1007/978-3-031-94575-5_15'
apa: Kapoor, S., Sharma, A., Röder, M., Demir, C., & Ngonga Ngomo, A.-C. (2025).
Robustness Evaluation of Knowledge Graph Embedding Models Under Non-targeted Attacks.
Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-94575-5_15
bibtex: '@inproceedings{Kapoor_Sharma_Röder_Demir_Ngonga Ngomo_2025, place={Cham},
title={Robustness Evaluation of Knowledge Graph Embedding Models Under Non-targeted
Attacks}, DOI={10.1007/978-3-031-94575-5_15},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Kapoor, Sourabh and Sharma, Arnab and Röder, Michael and Demir, Caglar
and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Kapoor, Sourabh, Arnab Sharma, Michael Röder, Caglar Demir, and Axel-Cyrille
Ngonga Ngomo. “Robustness Evaluation of Knowledge Graph Embedding Models Under
Non-Targeted Attacks.” In Lecture Notes in Computer Science. Cham: Springer
Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94575-5_15.'
ieee: 'S. Kapoor, A. Sharma, M. Röder, C. Demir, and A.-C. Ngonga Ngomo, “Robustness
Evaluation of Knowledge Graph Embedding Models Under Non-targeted Attacks,” 2025,
doi: 10.1007/978-3-031-94575-5_15.'
mla: Kapoor, Sourabh, et al. “Robustness Evaluation of Knowledge Graph Embedding
Models Under Non-Targeted Attacks.” Lecture Notes in Computer Science,
Springer Nature Switzerland, 2025, doi:10.1007/978-3-031-94575-5_15.
short: 'S. Kapoor, A. Sharma, M. Röder, C. Demir, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.'
date_created: 2026-01-12T17:24:11Z
date_updated: 2026-01-12T17:24:49Z
department:
- _id: '574'
- _id: '923'
doi: 10.1007/978-3-031-94575-5_15
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783031945748'
- '9783031945755'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Robustness Evaluation of Knowledge Graph Embedding Models Under Non-targeted
Attacks
type: conference
user_id: '67199'
year: '2025'
...
---
_id: '63573'
author:
- first_name: Adel
full_name: Memariani, Adel
last_name: Memariani
- first_name: Michael
full_name: Röder, Michael
id: '67199'
last_name: Röder
orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Memariani A, Röder M, Sharma A, Demir C, Ngonga Ngomo A-C. Link Prediction
Under Non-targeted Attacks: Do Soft Labels Always Help? In: Lecture Notes in
Computer Science. Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-09527-5_6'
apa: 'Memariani, A., Röder, M., Sharma, A., Demir, C., & Ngonga Ngomo, A.-C.
(2025). Link Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?
Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-032-09527-5_6'
bibtex: '@inproceedings{Memariani_Röder_Sharma_Demir_Ngonga Ngomo_2025, place={Cham},
title={Link Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?},
DOI={10.1007/978-3-032-09527-5_6},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Memariani, Adel and Röder, Michael and Sharma, Arnab and Demir, Caglar
and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Memariani, Adel, Michael Röder, Arnab Sharma, Caglar Demir, and Axel-Cyrille
Ngonga Ngomo. “Link Prediction Under Non-Targeted Attacks: Do Soft Labels Always
Help?” In Lecture Notes in Computer Science. Cham: Springer Nature Switzerland,
2025. https://doi.org/10.1007/978-3-032-09527-5_6.'
ieee: 'A. Memariani, M. Röder, A. Sharma, C. Demir, and A.-C. Ngonga Ngomo, “Link
Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?,” 2025, doi:
10.1007/978-3-032-09527-5_6.'
mla: 'Memariani, Adel, et al. “Link Prediction Under Non-Targeted Attacks: Do Soft
Labels Always Help?” Lecture Notes in Computer Science, Springer Nature
Switzerland, 2025, doi:10.1007/978-3-032-09527-5_6.'
short: 'A. Memariani, M. Röder, A. Sharma, C. Demir, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.'
date_created: 2026-01-12T17:18:35Z
date_updated: 2026-01-12T17:24:46Z
department:
- _id: '574'
- _id: '923'
doi: 10.1007/978-3-032-09527-5_6
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783032095268'
- '9783032095275'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: 'Link Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?'
type: conference
user_id: '67199'
year: '2025'
...
---
_id: '54449'
author:
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Hamada Mohamed Abdelsamee
full_name: Zahera, Hamada Mohamed Abdelsamee
id: '72768'
last_name: Zahera
orcid: 0000-0003-0215-1278
- first_name: Adrian
full_name: Wilke, Adrian
id: '9101'
last_name: Wilke
orcid: 0000-0002-6575-807X
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Jiayi
full_name: Li, Jiayi
last_name: Li
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'KOUAGOU NJ, Demir C, Zahera HMA, et al. Universal Knowledge Graph Embeddings.
In: Companion Proceedings of the ACM on Web Conference 2024. ACM; 2024.
doi:10.1145/3589335.3651978'
apa: KOUAGOU, N. J., Demir, C., Zahera, H. M. A., Wilke, A., Heindorf, S., Li, J.,
& Ngonga Ngomo, A.-C. (2024). Universal Knowledge Graph Embeddings. Companion
Proceedings of the ACM on Web Conference 2024. Companion Proceedings of the
ACM on Web Conference 2024, Singapore. https://doi.org/10.1145/3589335.3651978
bibtex: '@inproceedings{KOUAGOU_Demir_Zahera_Wilke_Heindorf_Li_Ngonga Ngomo_2024,
title={Universal Knowledge Graph Embeddings}, DOI={10.1145/3589335.3651978},
booktitle={Companion Proceedings of the ACM on Web Conference 2024}, publisher={ACM},
author={KOUAGOU, N’Dah Jean and Demir, Caglar and Zahera, Hamada Mohamed Abdelsamee
and Wilke, Adrian and Heindorf, Stefan and Li, Jiayi and Ngonga Ngomo, Axel-Cyrille},
year={2024} }'
chicago: KOUAGOU, N’Dah Jean, Caglar Demir, Hamada Mohamed Abdelsamee Zahera, Adrian
Wilke, Stefan Heindorf, Jiayi Li, and Axel-Cyrille Ngonga Ngomo. “Universal Knowledge
Graph Embeddings.” In Companion Proceedings of the ACM on Web Conference 2024.
ACM, 2024. https://doi.org/10.1145/3589335.3651978.
ieee: 'N. J. KOUAGOU et al., “Universal Knowledge Graph Embeddings,” presented
at the Companion Proceedings of the ACM on Web Conference 2024, Singapore, 2024,
doi: 10.1145/3589335.3651978.'
mla: KOUAGOU, N’Dah Jean, et al. “Universal Knowledge Graph Embeddings.” Companion
Proceedings of the ACM on Web Conference 2024, ACM, 2024, doi:10.1145/3589335.3651978.
short: 'N.J. KOUAGOU, C. Demir, H.M.A. Zahera, A. Wilke, S. Heindorf, J. Li, A.-C.
Ngonga Ngomo, in: Companion Proceedings of the ACM on Web Conference 2024, ACM,
2024.'
conference:
end_date: 2024-05-17
location: Singapore
name: Companion Proceedings of the ACM on Web Conference 2024
start_date: 2024-05-13
date_created: 2024-05-26T18:52:47Z
date_updated: 2024-05-26T19:06:10Z
department:
- _id: '760'
- _id: '574'
doi: 10.1145/3589335.3651978
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/abs/10.1145/3589335.3651978
oa: '1'
publication: Companion Proceedings of the ACM on Web Conference 2024
publication_status: published
publisher: ACM
status: public
title: Universal Knowledge Graph Embeddings
type: conference
user_id: '11871'
year: '2024'
...
---
_id: '58049'
abstract:
- lang: eng
text: In recent years, knowledge graph embedding models have been successfully applied
in the transductive setting to tackle various challenging tasks including link
prediction, and query answering. Yet, the transductive setting does not allow
for reasoning over unseen entities, relations, let alone numerical or non-numerical
literals. Although increasing efforts are put into exploring inductive scenarios,
inference over unseen entities, relations, and literals has yet to come. This
limitation prohibits the existing methods from handling real-world dynamic knowledge
graphs involving heterogeneous information about the world. Here, we propose a
remedy to this limitation. We propose the attentive byte-pair encoding layer (BytE)
to construct a triple embedding from a sequence of byte-pair encoded subword units
of entities and relations. Compared to the conventional setting, BytE leads to
massive feature reuse via weight tying, since it forces a knowledge graph embedding
model to learn embeddings for subword units instead of entities and relations
directly. Consequently, the size of the embedding matrices are not anymore bound
to the unique number of entities and relations of a knowledge graph. Experimental
results show that BytE improves the link prediction performance of 4 knowledge
graph embedding models on datasets where the syntactic representations of triples
are semantically meaningful. However, benefits of training a knowledge graph embedding
model with BytE dissipate on knowledge graphs where entities and relations are
represented with plain numbers or URIs. We provide an open source implementation
of BytE to foster reproducible research.
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: Demir C, KOUAGOU NJ, Sharma A, Ngonga Ngomo A-C. Inference over Unseen Entities,
Relations and Literals on Knowledge Graphs. Arxiv. Published online 2024.
doi:10.48550/ARXIV.2410.06742
apa: Demir, C., KOUAGOU, N. J., Sharma, A., & Ngonga Ngomo, A.-C. (2024). Inference
over Unseen Entities, Relations and Literals on Knowledge Graphs. Arxiv.
https://doi.org/10.48550/ARXIV.2410.06742
bibtex: '@article{Demir_KOUAGOU_Sharma_Ngonga Ngomo_2024, title={Inference over
Unseen Entities, Relations and Literals on Knowledge Graphs}, DOI={10.48550/ARXIV.2410.06742},
journal={Arxiv}, author={Demir, Caglar and KOUAGOU, N’Dah Jean and Sharma, Arnab
and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
chicago: Demir, Caglar, N’Dah Jean KOUAGOU, Arnab Sharma, and Axel-Cyrille Ngonga
Ngomo. “Inference over Unseen Entities, Relations and Literals on Knowledge Graphs.”
Arxiv, 2024. https://doi.org/10.48550/ARXIV.2410.06742.
ieee: 'C. Demir, N. J. KOUAGOU, A. Sharma, and A.-C. Ngonga Ngomo, “Inference over
Unseen Entities, Relations and Literals on Knowledge Graphs,” Arxiv, 2024,
doi: 10.48550/ARXIV.2410.06742.'
mla: Demir, Caglar, et al. “Inference over Unseen Entities, Relations and Literals
on Knowledge Graphs.” Arxiv, 2024, doi:10.48550/ARXIV.2410.06742.
short: C. Demir, N.J. KOUAGOU, A. Sharma, A.-C. Ngonga Ngomo, Arxiv (2024).
date_created: 2025-01-06T12:19:39Z
date_updated: 2025-01-07T20:01:36Z
doi: 10.48550/ARXIV.2410.06742
language:
- iso: eng
publication: Arxiv
status: public
title: Inference over Unseen Entities, Relations and Literals on Knowledge Graphs
type: journal_article
user_id: '67200'
year: '2024'
...
---
_id: '58051'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: Demir C, Sharma A, Ngonga Ngomo A-C. Adaptive Stochastic Weight Averaging.
arxiv. Published online 2024. doi:10.48550/ARXIV.2406.19092
apa: Demir, C., Sharma, A., & Ngonga Ngomo, A.-C. (2024). Adaptive Stochastic
Weight Averaging. Arxiv. https://doi.org/10.48550/ARXIV.2406.19092
bibtex: '@article{Demir_Sharma_Ngonga Ngomo_2024, title={Adaptive Stochastic Weight
Averaging}, DOI={10.48550/ARXIV.2406.19092},
journal={arxiv}, author={Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille},
year={2024} }'
chicago: Demir, Caglar, Arnab Sharma, and Axel-Cyrille Ngonga Ngomo. “Adaptive Stochastic
Weight Averaging.” Arxiv, 2024. https://doi.org/10.48550/ARXIV.2406.19092.
ieee: 'C. Demir, A. Sharma, and A.-C. Ngonga Ngomo, “Adaptive Stochastic Weight
Averaging,” arxiv, 2024, doi: 10.48550/ARXIV.2406.19092.'
mla: Demir, Caglar, et al. “Adaptive Stochastic Weight Averaging.” Arxiv,
2024, doi:10.48550/ARXIV.2406.19092.
short: C. Demir, A. Sharma, A.-C. Ngonga Ngomo, Arxiv (2024).
date_created: 2025-01-06T12:20:24Z
date_updated: 2025-01-07T20:00:53Z
doi: 10.48550/ARXIV.2406.19092
language:
- iso: eng
publication: arxiv
status: public
title: Adaptive Stochastic Weight Averaging
type: journal_article
user_id: '67200'
year: '2024'
...
---
_id: '55653'
abstract:
- lang: eng
text: We consider the problem of class expression learning using cardinality-minimal
sets of examples. Recent class expression learning approaches employ deep neural
networks and have demonstrated tremendous performance improvements in execution
time and quality of the computed solutions. However, they lack generalization
capabilities when it comes to the number of examples used in a learning problem,
i.e., they often perform poorly on unseen learning problems where only a few examples
are given. In this work, we propose a generalization of the classical class expression
learning problem to address the limitations above. In short, our generalized learning
problem (GLP) forces learning systems to solve the classical class expression
learning problem using the smallest possible subsets of examples, thereby improving
the learning systems' ability to solve unseen learning problems with arbitrary
numbers of examples. Moreover, we develop ROCES, a learning algorithm for synthesis-based
approaches to solve GLP. Experimental results suggest that post training, ROCES
outperforms existing synthesis-based approaches on out-of-distribution learning
problems while remaining highly competitive overall.
author:
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. ROCES: Robust Class Expression
Synthesis in Description Logics via Iterative Sampling. In: Proceedings of
the Thirty-ThirdInternational Joint Conference on Artificial Intelligence.
International Joint Conferences on Artificial Intelligence Organization; 2024.
doi:10.24963/ijcai.2024/479'
apa: 'KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2024).
ROCES: Robust Class Expression Synthesis in Description Logics via Iterative Sampling.
Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial
Intelligence. https://doi.org/10.24963/ijcai.2024/479'
bibtex: '@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2024, title={ROCES:
Robust Class Expression Synthesis in Description Logics via Iterative Sampling},
DOI={10.24963/ijcai.2024/479},
booktitle={Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial
Intelligence}, publisher={International Joint Conferences on Artificial Intelligence
Organization}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan and Demir, Caglar
and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
chicago: 'KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga
Ngomo. “ROCES: Robust Class Expression Synthesis in Description Logics via Iterative
Sampling.” In Proceedings of the Thirty-ThirdInternational Joint Conference
on Artificial Intelligence. International Joint Conferences on Artificial
Intelligence Organization, 2024. https://doi.org/10.24963/ijcai.2024/479.'
ieee: 'N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “ROCES: Robust
Class Expression Synthesis in Description Logics via Iterative Sampling,” 2024,
doi: 10.24963/ijcai.2024/479.'
mla: 'KOUAGOU, N’Dah Jean, et al. “ROCES: Robust Class Expression Synthesis in Description
Logics via Iterative Sampling.” Proceedings of the Thirty-ThirdInternational
Joint Conference on Artificial Intelligence, International Joint Conferences
on Artificial Intelligence Organization, 2024, doi:10.24963/ijcai.2024/479.'
short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: Proceedings
of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence,
International Joint Conferences on Artificial Intelligence Organization, 2024.'
date_created: 2024-08-19T12:43:55Z
date_updated: 2025-07-18T15:52:39Z
ddc:
- '000'
doi: 10.24963/ijcai.2024/479
file:
- access_level: closed
content_type: application/pdf
creator: nkouagou
date_created: 2025-06-26T08:06:07Z
date_updated: 2025-06-26T08:06:07Z
file_id: '60394'
file_name: public.pdf
file_size: 400277
relation: main_file
success: 1
file_date_updated: 2025-06-26T08:06:07Z
has_accepted_license: '1'
language:
- iso: eng
publication: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial
Intelligence
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence Organization
status: public
title: 'ROCES: Robust Class Expression Synthesis in Description Logics via Iterative
Sampling'
type: conference
user_id: '11871'
year: '2024'
...
---
_id: '46460'
author:
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: N'Dah Jean
full_name: Kouagou, N'Dah Jean
id: '87189'
last_name: Kouagou
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Nikoloas
full_name: Karalis, Nikoloas
last_name: Karalis
- first_name: Alexander
full_name: Bigerl, Alexander
id: '72857'
last_name: Bigerl
citation:
ama: 'Ngonga Ngomo A-C, Demir C, Kouagou NJ, Heindorf S, Karalis N, Bigerl A. Class
Expression Learning with Multiple Representations. In: Compendium of Neurosymbolic
Artificial Intelligence. IOS Press; 2023:272–286.'
apa: Ngonga Ngomo, A.-C., Demir, C., Kouagou, N. J., Heindorf, S., Karalis, N.,
& Bigerl, A. (2023). Class Expression Learning with Multiple Representations.
In Compendium of Neurosymbolic Artificial Intelligence (pp. 272–286). IOS
Press.
bibtex: '@inbook{Ngonga Ngomo_Demir_Kouagou_Heindorf_Karalis_Bigerl_2023, title={Class
Expression Learning with Multiple Representations}, booktitle={Compendium of Neurosymbolic
Artificial Intelligence}, publisher={IOS Press}, author={Ngonga Ngomo, Axel-Cyrille
and Demir, Caglar and Kouagou, N’Dah Jean and Heindorf, Stefan and Karalis, Nikoloas
and Bigerl, Alexander}, year={2023}, pages={272–286} }'
chicago: Ngonga Ngomo, Axel-Cyrille, Caglar Demir, N’Dah Jean Kouagou, Stefan Heindorf,
Nikoloas Karalis, and Alexander Bigerl. “Class Expression Learning with Multiple
Representations.” In Compendium of Neurosymbolic Artificial Intelligence,
272–286. IOS Press, 2023.
ieee: A.-C. Ngonga Ngomo, C. Demir, N. J. Kouagou, S. Heindorf, N. Karalis, and
A. Bigerl, “Class Expression Learning with Multiple Representations,” in Compendium
of Neurosymbolic Artificial Intelligence, IOS Press, 2023, pp. 272–286.
mla: Ngonga Ngomo, Axel-Cyrille, et al. “Class Expression Learning with Multiple
Representations.” Compendium of Neurosymbolic Artificial Intelligence,
IOS Press, 2023, pp. 272–286.
short: 'A.-C. Ngonga Ngomo, C. Demir, N.J. Kouagou, S. Heindorf, N. Karalis, A.
Bigerl, in: Compendium of Neurosymbolic Artificial Intelligence, IOS Press, 2023,
pp. 272–286.'
date_created: 2023-08-08T11:49:51Z
date_updated: 2023-11-21T08:06:20Z
department:
- _id: '760'
- _id: '574'
language:
- iso: eng
page: 272–286
publication: Compendium of Neurosymbolic Artificial Intelligence
publisher: IOS Press
status: public
title: Class Expression Learning with Multiple Representations
type: book_chapter
user_id: '14931'
year: '2023'
...
---
_id: '46248'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Michel
full_name: Wiebesiek, Michel
last_name: Wiebesiek
- first_name: Renzhong
full_name: Lu, Renzhong
last_name: Lu
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
citation:
ama: 'Demir C, Wiebesiek M, Lu R, Ngonga Ngomo A-C, Heindorf S. LitCQD: Multi-Hop
Reasoning in Incomplete Knowledge Graphs with Numeric Literals. ECML PKDD.
Published online 2023.'
apa: 'Demir, C., Wiebesiek, M., Lu, R., Ngonga Ngomo, A.-C., & Heindorf, S.
(2023). LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric
Literals. ECML PKDD. European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases, Torino.'
bibtex: '@article{Demir_Wiebesiek_Lu_Ngonga Ngomo_Heindorf_2023, title={LitCQD:
Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals}, journal={ECML
PKDD}, author={Demir, Caglar and Wiebesiek, Michel and Lu, Renzhong and Ngonga
Ngomo, Axel-Cyrille and Heindorf, Stefan}, year={2023} }'
chicago: 'Demir, Caglar, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo,
and Stefan Heindorf. “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs
with Numeric Literals.” ECML PKDD, 2023.'
ieee: 'C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, and S. Heindorf, “LitCQD:
Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals,” ECML
PKDD, 2023.'
mla: 'Demir, Caglar, et al. “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge
Graphs with Numeric Literals.” ECML PKDD, 2023.'
short: C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, S. Heindorf, ECML PKDD
(2023).
conference:
location: Torino
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
date_created: 2023-08-01T09:24:21Z
date_updated: 2024-03-06T16:18:53Z
ddc:
- '000'
department:
- _id: '574'
- _id: '760'
file:
- access_level: open_access
content_type: application/pdf
creator: cdemir
date_created: 2023-08-01T09:24:15Z
date_updated: 2023-08-01T09:24:15Z
file_id: '46249'
file_name: public.pdf
file_size: 562759
relation: main_file
file_date_updated: 2023-08-01T09:24:15Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '407'
grant_number: '101070305'
name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs'
- _id: '410'
grant_number: '860801'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems'
publication: ECML PKDD
status: public
title: 'LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals'
type: journal_article
user_id: '14931'
year: '2023'
...
---
_id: '47421'
abstract:
- lang: eng
text: Class expression learning in description logics has long been regarded as
an iterative search problem in an infinite conceptual space. Each iteration of
the search process invokes a reasoner and a heuristic function. The reasoner finds
the instances of the current expression, and the heuristic function computes the
information gain and decides on the next step to be taken. As the size of the
background knowledge base grows, search-based approaches for class expression
learning become prohibitively slow. Current neural class expression synthesis
(NCES) approaches investigate the use of neural networks for class expression
learning in the attributive language with complement (ALC). While they show significant
improvements over search-based approaches in runtime and quality of the computed
solutions, they rely on the availability of pretrained embeddings for the input
knowledge base. Moreover, they are not applicable to ontologies in more expressive
description logics. In this paper, we propose a novel NCES approach which extends
the state of the art to the description logic ALCHIQ(D). Our extension, dubbed
NCES2, comes with an improved training data generator and does not require pretrained
embeddings for the input knowledge base as both the embedding model and the class
expression synthesizer are trained jointly. Empirical results on benchmark datasets
suggest that our approach inherits the scalability capability of current NCES
instances with the additional advantage that it supports more complex learning
problems. NCES2 achieves the highest performance overall when compared to search-based
approaches and to its predecessor NCES. We provide our source code, datasets,
and pretrained models at https://github.com/dice-group/NCES2.
author:
- first_name: N'Dah Jean
full_name: Kouagou, N'Dah Jean
id: '87189'
last_name: Kouagou
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Kouagou NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression
Synthesis in ALCHIQ(D). In: Machine Learning and Knowledge Discovery in Databases:
Research Track. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-43421-1_12'
apa: 'Kouagou, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023).
Neural Class Expression Synthesis in ALCHIQ(D). In Machine Learning and Knowledge
Discovery in Databases: Research Track. European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases, Turin. Springer
Nature Switzerland. https://doi.org/10.1007/978-3-031-43421-1_12'
bibtex: '@inbook{Kouagou_Heindorf_Demir_Ngonga Ngomo_2023, place={Cham}, title={Neural
Class Expression Synthesis in ALCHIQ(D)}, DOI={10.1007/978-3-031-43421-1_12},
booktitle={Machine Learning and Knowledge Discovery in Databases: Research Track},
publisher={Springer Nature Switzerland}, author={Kouagou, N’Dah Jean and Heindorf,
Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: 'Kouagou, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga
Ngomo. “Neural Class Expression Synthesis in ALCHIQ(D).” In Machine Learning
and Knowledge Discovery in Databases: Research Track. Cham: Springer Nature
Switzerland, 2023. https://doi.org/10.1007/978-3-031-43421-1_12.'
ieee: 'N. J. Kouagou, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class
Expression Synthesis in ALCHIQ(D),” in Machine Learning and Knowledge Discovery
in Databases: Research Track, Cham: Springer Nature Switzerland, 2023.'
mla: 'Kouagou, N’Dah Jean, et al. “Neural Class Expression Synthesis in ALCHIQ(D).”
Machine Learning and Knowledge Discovery in Databases: Research Track,
Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-43421-1_12.'
short: 'N.J. Kouagou, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: Machine Learning
and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland,
Cham, 2023.'
conference:
end_date: 2023-09-22
location: Turin
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2023-09-18
date_created: 2023-09-25T13:42:01Z
date_updated: 2024-05-22T10:48:24Z
ddc:
- '000'
department:
- _id: '760'
- _id: '574'
doi: 10.1007/978-3-031-43421-1_12
file:
- access_level: open_access
content_type: application/pdf
creator: heindorf
date_created: 2024-05-22T10:45:08Z
date_updated: 2024-05-22T10:46:58Z
file_id: '54417'
file_name: NCES2_public.pdf
file_size: 432708
relation: main_file
file_date_updated: 2024-05-22T10:46:58Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2023/ECML_NCES2/NCES2_public.pdf
oa: '1'
place: Cham
publication: 'Machine Learning and Knowledge Discovery in Databases: Research Track'
publication_identifier:
isbn:
- '9783031434204'
- '9783031434211'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Neural Class Expression Synthesis in ALCHIQ(D)
type: book_chapter
user_id: '11871'
year: '2023'
...
---
_id: '54612'
author:
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression
Synthesis (Extended Abstract). In: NeSy 2023, 17th International Workshop on
Neural-Symbolic Learning and Reasoning, Certosa Di Pontignano, Siena, Italy.
CEUR-WS; 2023.'
apa: KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023).
Neural Class Expression Synthesis (Extended Abstract). NeSy 2023, 17th International
Workshop on Neural-Symbolic Learning and Reasoning, Certosa Di Pontignano, Siena,
Italy.
bibtex: '@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural
Class Expression Synthesis (Extended Abstract)}, booktitle={NeSy 2023, 17th International
Workshop on Neural-Symbolic Learning and Reasoning, Certosa di Pontignano, Siena,
Italy}, publisher={CEUR-WS}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan
and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga
Ngomo. “Neural Class Expression Synthesis (Extended Abstract).” In NeSy 2023,
17th International Workshop on Neural-Symbolic Learning and Reasoning, Certosa
Di Pontignano, Siena, Italy. CEUR-WS, 2023.
ieee: N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class
Expression Synthesis (Extended Abstract),” 2023.
mla: KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis (Extended Abstract).”
NeSy 2023, 17th International Workshop on Neural-Symbolic Learning and Reasoning,
Certosa Di Pontignano, Siena, Italy, CEUR-WS, 2023.
short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: NeSy 2023,
17th International Workshop on Neural-Symbolic Learning and Reasoning, Certosa
Di Pontignano, Siena, Italy, CEUR-WS, 2023.'
date_created: 2024-06-04T15:36:52Z
date_updated: 2024-06-04T15:40:30Z
department:
- _id: '574'
- _id: '760'
keyword:
- 318 SFB-TRR demir dice enexa heindorf knowgraphs kouagou ngonga sail
language:
- iso: eng
publication: NeSy 2023, 17th International Workshop on Neural-Symbolic Learning and
Reasoning, Certosa di Pontignano, Siena, Italy
publisher: CEUR-WS
status: public
title: Neural Class Expression Synthesis (Extended Abstract)
type: conference
user_id: '67199'
year: '2023'
...
---
_id: '54615'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Demir C, Ngonga Ngomo A-C. Learning Permutation-Invariant Embeddings for Description
Logic Concepts. In: Advances in Intelligent Data Analysis XXI: 21st International
Symposium on Intelligent Data Analysis, IDA 2023, Louvain-La-Neuve, Belgium, April
12–14, 2023, Proceedings. ; 2023:103–115.'
apa: 'Demir, C., & Ngonga Ngomo, A.-C. (2023). Learning Permutation-Invariant
Embeddings for Description Logic Concepts. Advances in Intelligent Data Analysis
XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-La-Neuve,
Belgium, April 12–14, 2023, Proceedings, 103–115.'
bibtex: '@inproceedings{Demir_Ngonga Ngomo_2023, title={Learning Permutation-Invariant
Embeddings for Description Logic Concepts}, booktitle={Advances in Intelligent
Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis,
IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings}, author={Demir,
Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023}, pages={103–115} }'
chicago: 'Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Learning Permutation-Invariant
Embeddings for Description Logic Concepts.” In Advances in Intelligent Data
Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023,
Louvain-La-Neuve, Belgium, April 12–14, 2023, Proceedings, 103–115, 2023.'
ieee: 'C. Demir and A.-C. Ngonga Ngomo, “Learning Permutation-Invariant Embeddings
for Description Logic Concepts,” in Advances in Intelligent Data Analysis XXI:
21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve,
Belgium, April 12–14, 2023, Proceedings, 2023, pp. 103–115.'
mla: 'Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Learning Permutation-Invariant
Embeddings for Description Logic Concepts.” Advances in Intelligent Data Analysis
XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-La-Neuve,
Belgium, April 12–14, 2023, Proceedings, 2023, pp. 103–115.'
short: 'C. Demir, A.-C. Ngonga Ngomo, in: Advances in Intelligent Data Analysis
XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-La-Neuve,
Belgium, April 12–14, 2023, Proceedings, 2023, pp. 103–115.'
date_created: 2024-06-04T15:58:48Z
date_updated: 2024-06-04T15:59:04Z
department:
- _id: '574'
keyword:
- 318 SFB-TRR demir dice enexa ngonga sail
language:
- iso: eng
page: 103–115
publication: 'Advances in Intelligent Data Analysis XXI: 21st International Symposium
on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14,
2023, Proceedings'
status: public
title: Learning Permutation-Invariant Embeddings for Description Logic Concepts
type: conference
user_id: '67199'
year: '2023'
...
---
_id: '33734'
abstract:
- lang: eng
text: 'Many applications require explainable node classification in knowledge graphs.
Towards this end, a popular ``white-box'''' approach is class expression learning:
Given sets of positive and negative nodes, class expressions in description logics
are learned that separate positive from negative nodes. Most existing approaches
are search-based approaches generating many candidate class expressions and selecting
the best one. However, they often take a long time to find suitable class expressions.
In this paper, we cast class expression learning as a translation problem and
propose a new family of class expression learning approaches which we dub neural
class expression synthesizers. Training examples are ``translated'''' into class
expressions in a fashion akin to machine translation. Consequently, our synthesizers
are not subject to the runtime limitations of search-based approaches. We study
three instances of this novel family of approaches based on LSTMs, GRUs, and set
transformers, respectively. An evaluation of our approach on four benchmark datasets
suggests that it can effectively synthesize high-quality class expressions with
respect to the input examples in approximately one second on average. Moreover,
a comparison to state-of-the-art approaches suggests that we achieve better F-measures
on large datasets. For reproducibility purposes, we provide our implementation
as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis'
author:
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression
Synthesis. In: Pesquita C, Jimenez-Ruiz E, McCusker J, et al., eds. The Semantic
Web - 20th Extended Semantic Web Conference (ESWC 2023). Vol 13870. Springer
International Publishing; 2023:209-226. doi:https://doi.org/10.1007/978-3-031-33455-9_13'
apa: KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023).
Neural Class Expression Synthesis. In C. Pesquita, E. Jimenez-Ruiz, J. McCusker,
D. Faria, M. Dragoni, A. Dimou, R. Troncy, & S. Hertling (Eds.), The Semantic
Web - 20th Extended Semantic Web Conference (ESWC 2023) (Vol. 13870, pp. 209–226).
Springer International Publishing. https://doi.org/10.1007/978-3-031-33455-9_13
bibtex: '@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural
Class Expression Synthesis}, volume={13870}, DOI={https://doi.org/10.1007/978-3-031-33455-9_13},
booktitle={The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)},
publisher={Springer International Publishing}, author={KOUAGOU, N’Dah Jean and
Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, editor={Pesquita,
Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni,
Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}, year={2023},
pages={209–226} }'
chicago: KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga
Ngomo. “Neural Class Expression Synthesis.” In The Semantic Web - 20th Extended
Semantic Web Conference (ESWC 2023), edited by Catia Pesquita, Ernesto Jimenez-Ruiz,
Jamie McCusker, Daniel Faria, Mauro Dragoni, Anastasia Dimou, Raphael Troncy,
and Sven Hertling, 13870:209–26. Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-33455-9_13.
ieee: 'N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class
Expression Synthesis,” in The Semantic Web - 20th Extended Semantic Web Conference
(ESWC 2023), Hersonissos, Crete, Greece, 2023, vol. 13870, pp. 209–226, doi:
https://doi.org/10.1007/978-3-031-33455-9_13.'
mla: KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis.” The Semantic
Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita
et al., vol. 13870, Springer International Publishing, 2023, pp. 209–26, doi:https://doi.org/10.1007/978-3-031-33455-9_13.
short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: C. Pesquita,
E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, S. Hertling
(Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023),
Springer International Publishing, 2023, pp. 209–226.'
conference:
end_date: 2023-06-01
location: Hersonissos, Crete, Greece
name: 20th Extended Semantic Web Conference
start_date: 2023-05-28
date_created: 2022-10-15T19:20:11Z
date_updated: 2023-07-02T18:10:02Z
department:
- _id: '574'
- _id: '760'
doi: https://doi.org/10.1007/978-3-031-33455-9_13
editor:
- first_name: Catia
full_name: Pesquita, Catia
last_name: Pesquita
- first_name: Ernesto
full_name: Jimenez-Ruiz, Ernesto
last_name: Jimenez-Ruiz
- first_name: Jamie
full_name: McCusker, Jamie
last_name: McCusker
- first_name: Daniel
full_name: Faria, Daniel
last_name: Faria
- first_name: Mauro
full_name: Dragoni, Mauro
last_name: Dragoni
- first_name: Anastasia
full_name: Dimou, Anastasia
last_name: Dimou
- first_name: Raphael
full_name: Troncy, Raphael
last_name: Troncy
- first_name: Sven
full_name: Hertling, Sven
last_name: Hertling
external_id:
unknown:
- https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13
intvolume: ' 13870'
keyword:
- Neural network
- Concept learning
- Description logics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf
oa: '1'
page: 209 - 226
project:
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
- _id: '407'
grant_number: '101070305'
name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs'
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems'
publication: The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)
publication_identifier:
unknown:
- 978-3-031-33455-9
publication_status: published
publisher: Springer International Publishing
status: public
title: Neural Class Expression Synthesis
type: conference
user_id: '11871'
volume: 13870
year: '2023'
...
---
_id: '46243'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: Demir C, Ngonga Ngomo A-C. Clifford Embeddings – A Generalized Approach for
Embedding in Normed Algebras. ECML-PKDD. Published online 2023.
apa: Demir, C., & Ngonga Ngomo, A.-C. (2023). Clifford Embeddings – A Generalized
Approach for Embedding in Normed Algebras. ECML-PKDD. European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases,
Torino.
bibtex: '@article{Demir_Ngonga Ngomo_2023, title={Clifford Embeddings – A Generalized
Approach for Embedding in Normed Algebras}, journal={ECML-PKDD}, author={Demir,
Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Clifford Embeddings – A
Generalized Approach for Embedding in Normed Algebras.” ECML-PKDD, 2023.
ieee: C. Demir and A.-C. Ngonga Ngomo, “Clifford Embeddings – A Generalized Approach
for Embedding in Normed Algebras,” ECML-PKDD, 2023.
mla: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Clifford Embeddings – A Generalized
Approach for Embedding in Normed Algebras.” ECML-PKDD, 2023.
short: C. Demir, A.-C. Ngonga Ngomo, ECML-PKDD (2023).
conference:
location: Torino
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
date_created: 2023-08-01T09:12:06Z
date_updated: 2023-08-01T09:22:40Z
ddc:
- '000'
file:
- access_level: open_access
content_type: application/pdf
creator: cdemir
date_created: 2023-08-01T09:11:59Z
date_updated: 2023-08-01T09:11:59Z
file_id: '46244'
file_name: public.pdf
file_size: 408352
relation: main_file
file_date_updated: 2023-08-01T09:11:59Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '407'
grant_number: '101070305'
name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs'
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems'
publication: ECML-PKDD
status: public
title: Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras
type: journal_article
user_id: '43817'
year: '2023'
...
---
_id: '46251'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: Demir C, Ngonga Ngomo A-C. Neuro-Symbolic Class Expression Learning. International
Joint Conference on Artificial Intelligence. Published online 2023.
apa: Demir, C., & Ngonga Ngomo, A.-C. (2023). Neuro-Symbolic Class Expression
Learning. International Joint Conference on Artificial Intelligence. International
Joint Conference on Artificial Intelligence IJCAI 2023, Macau.
bibtex: '@article{Demir_Ngonga Ngomo_2023, title={Neuro-Symbolic Class Expression
Learning}, journal={International Joint Conference on Artificial Intelligence},
author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression
Learning.” International Joint Conference on Artificial Intelligence, 2023.
ieee: C. Demir and A.-C. Ngonga Ngomo, “Neuro-Symbolic Class Expression Learning,”
International Joint Conference on Artificial Intelligence, 2023.
mla: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression
Learning.” International Joint Conference on Artificial Intelligence, 2023.
short: C. Demir, A.-C. Ngonga Ngomo, International Joint Conference on Artificial
Intelligence (2023).
conference:
location: Macau
name: International Joint Conference on Artificial Intelligence IJCAI 2023
date_created: 2023-08-01T09:30:37Z
date_updated: 2023-08-01T09:44:30Z
ddc:
- '000'
department:
- _id: '574'
file:
- access_level: open_access
content_type: application/pdf
creator: cdemir
date_created: 2023-08-01T09:30:35Z
date_updated: 2023-08-01T09:30:35Z
file_id: '46252'
file_name: public.pdf
file_size: 340865
relation: main_file
file_date_updated: 2023-08-01T09:30:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '407'
grant_number: '101070305'
name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs'
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems'
publication: International Joint Conference on Artificial Intelligence
status: public
title: Neuro-Symbolic Class Expression Learning
type: journal_article
user_id: '43817'
year: '2023'
...
---
_id: '31545'
abstract:
- lang: eng
text: Knowledge graph embedding research has mainly focused on learning continuous
representations of entities and relations tailored towards the link prediction
problem. Recent results indicate an ever increasing predictive ability of current
approaches on benchmark datasets. However, this effectiveness often comes with
the cost of over-parameterization and increased computationally complexity. The
former induces extensive hyperparameter optimization to mitigate malicious overfitting.
The latter magnifies the importance of winning the hardware lottery. Here, we
investigate a remedy for the first problem. We propose a technique based on Kronecker
decomposition to reduce the number of parameters in a knowledge graph embedding
model, while retaining its expressiveness. Through Kronecker decomposition, large
embedding matrices are split into smaller embedding matrices during the training
process. Hence, embeddings of knowledge graphs are not plainly retrieved but reconstructed
on the fly. The decomposition ensures that elementwise interactions between three
embedding vectors are extended with interactions within each embedding vector.
This implicitly reduces redundancy in embedding vectors and encourages feature
reuse. To quantify the impact of applying Kronecker decomposition on embedding
matrices, we conduct a series of experiments on benchmark datasets. Our experiments
suggest that applying Kronecker decomposition on embedding matrices leads to an
improved parameter efficiency on all benchmark datasets. Moreover, empirical evidence
suggests that reconstructed embeddings entail robustness against noise in the
input knowledge graph. To foster reproducible research, we provide an open-source
implementation of our approach, including training and evaluation scripts as well
as pre-trained models in our knowledge graph embedding framework.
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: Demir C, Lienen J, Ngonga Ngomo A-C. Kronecker Decomposition for Knowledge
Graph Embeddings. arXiv:220506560. Published online 2022.
apa: Demir, C., Lienen, J., & Ngonga Ngomo, A.-C. (2022). Kronecker Decomposition
for Knowledge Graph Embeddings. In arXiv:2205.06560.
bibtex: '@article{Demir_Lienen_Ngonga Ngomo_2022, title={Kronecker Decomposition
for Knowledge Graph Embeddings}, journal={arXiv:2205.06560}, author={Demir, Caglar
and Lienen, Julian and Ngonga Ngomo, Axel-Cyrille}, year={2022} }'
chicago: Demir, Caglar, Julian Lienen, and Axel-Cyrille Ngonga Ngomo. “Kronecker
Decomposition for Knowledge Graph Embeddings.” ArXiv:2205.06560, 2022.
ieee: C. Demir, J. Lienen, and A.-C. Ngonga Ngomo, “Kronecker Decomposition for
Knowledge Graph Embeddings,” arXiv:2205.06560. 2022.
mla: Demir, Caglar, et al. “Kronecker Decomposition for Knowledge Graph Embeddings.”
ArXiv:2205.06560, 2022.
short: C. Demir, J. Lienen, A.-C. Ngonga Ngomo, ArXiv:2205.06560 (2022).
date_created: 2022-05-31T07:04:36Z
date_updated: 2022-05-31T07:05:50Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2205.06560
oa: '1'
publication: arXiv:2205.06560
status: public
title: Kronecker Decomposition for Knowledge Graph Embeddings
type: preprint
user_id: '44040'
year: '2022'
...
---
_id: '31546'
abstract:
- lang: eng
text: In semi-supervised learning, the paradigm of self-training refers to the idea
of learning from pseudo-labels suggested by the learner itself. Across various
domains, corresponding methods have proven effective and achieve state-of-the-art
performance. However, pseudo-labels typically stem from ad-hoc heuristics, relying
on the quality of the predictions though without guaranteeing their validity.
One such method, so-called credal self-supervised learning, maintains pseudo-supervision
in the form of sets of (instead of single) probability distributions over labels,
thereby allowing for a flexible yet uncertainty-aware labeling. Again, however,
there is no justification beyond empirical effectiveness. To address this deficiency,
we make use of conformal prediction, an approach that comes with guarantees on
the validity of set-valued predictions. As a result, the construction of credal
sets of labels is supported by a rigorous theoretical foundation, leading to better
calibrated and less error-prone supervision for unlabeled data. Along with this,
we present effective algorithms for learning from credal self-supervision. An
empirical study demonstrates excellent calibration properties of the pseudo-supervision,
as well as the competitiveness of our method on several benchmark datasets.
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning.
arXiv:220515239. Published online 2022.
apa: Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised
Learning. In arXiv:2205.15239.
bibtex: '@article{Lienen_Demir_Hüllermeier_2022, title={Conformal Credal Self-Supervised
Learning}, journal={arXiv:2205.15239}, author={Lienen, Julian and Demir, Caglar
and Hüllermeier, Eyke}, year={2022} }'
chicago: Lienen, Julian, Caglar Demir, and Eyke Hüllermeier. “Conformal Credal Self-Supervised
Learning.” ArXiv:2205.15239, 2022.
ieee: J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised
Learning,” arXiv:2205.15239. 2022.
mla: Lienen, Julian, et al. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239,
2022.
short: J. Lienen, C. Demir, E. Hüllermeier, ArXiv:2205.15239 (2022).
date_created: 2022-05-31T07:05:36Z
date_updated: 2022-05-31T07:05:54Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2205.15239
oa: '1'
publication: arXiv:2205.15239
status: public
title: Conformal Credal Self-Supervised Learning
type: preprint
user_id: '44040'
year: '2022'
...
---
_id: '33740'
author:
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Learning Concept Lengths
Accelerates Concept Learning in ALC. In: The Semantic Web. Springer International
Publishing; 2022. doi:10.1007/978-3-031-06981-9_14'
apa: KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2022).
Learning Concept Lengths Accelerates Concept Learning in ALC. In The Semantic
Web. Springer International Publishing. https://doi.org/10.1007/978-3-031-06981-9_14
bibtex: '@inbook{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2022, place={Cham}, title={Learning
Concept Lengths Accelerates Concept Learning in ALC}, DOI={10.1007/978-3-031-06981-9_14},
booktitle={The Semantic Web}, publisher={Springer International Publishing}, author={KOUAGOU,
N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
year={2022} }'
chicago: 'KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga
Ngomo. “Learning Concept Lengths Accelerates Concept Learning in ALC.” In The
Semantic Web. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-06981-9_14.'
ieee: 'N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Learning Concept
Lengths Accelerates Concept Learning in ALC,” in The Semantic Web, Cham:
Springer International Publishing, 2022.'
mla: KOUAGOU, N’Dah Jean, et al. “Learning Concept Lengths Accelerates Concept Learning
in ALC.” The Semantic Web, Springer International Publishing, 2022, doi:10.1007/978-3-031-06981-9_14.
short: 'N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: The Semantic
Web, Springer International Publishing, Cham, 2022.'
date_created: 2022-10-15T19:34:41Z
date_updated: 2024-04-03T13:26:10Z
department:
- _id: '574'
- _id: '760'
doi: 10.1007/978-3-031-06981-9_14
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.04911
oa: '1'
place: Cham
publication: The Semantic Web
publication_identifier:
isbn:
- '9783031069802'
- '9783031069819'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-06981-9_14
status: public
title: Learning Concept Lengths Accelerates Concept Learning in ALC
type: book_chapter
user_id: '11871'
year: '2022'
...
---
_id: '29290'
abstract:
- lang: eng
text: "Classifying nodes in knowledge graphs is an important task, e.g., predicting\r\nmissing
types of entities, predicting which molecules cause cancer, or\r\npredicting which
drugs are promising treatment candidates. While black-box\r\nmodels often achieve
high predictive performance, they are only post-hoc and\r\nlocally explainable
and do not allow the learned model to be easily enriched\r\nwith domain knowledge.
Towards this end, learning description logic concepts\r\nfrom positive and negative
examples has been proposed. However, learning such\r\nconcepts often takes a long
time and state-of-the-art approaches provide\r\nlimited support for literal data
values, although they are crucial for many\r\napplications. In this paper, we
propose EvoLearner - an evolutionary approach\r\nto learn ALCQ(D), which is the
attributive language with complement (ALC)\r\npaired with qualified cardinality
restrictions (Q) and data properties (D). We\r\ncontribute a novel initialization
method for the initial population: starting\r\nfrom positive examples (nodes in
the knowledge graph), we perform biased random\r\nwalks and translate them to
description logic concepts. Moreover, we improve\r\nsupport for data properties
by maximizing information gain when deciding where\r\nto split the data. We show
that our approach significantly outperforms the\r\nstate of the art on the benchmarking
framework SML-Bench for structured machine\r\nlearning. Our ablation study confirms
that this is due to our novel\r\ninitialization method and support for data properties."
author:
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Lukas
full_name: Blübaum, Lukas
last_name: Blübaum
- first_name: Nick
full_name: Düsterhus, Nick
last_name: Düsterhus
- first_name: Till
full_name: Werner, Till
last_name: Werner
- first_name: Varun Nandkumar
full_name: Golani, Varun Nandkumar
last_name: Golani
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Heindorf S, Blübaum L, Düsterhus N, et al. EvoLearner: Learning Description
Logics with Evolutionary Algorithms. In: WWW. ACM; 2022:818-828. doi:10.1145/3485447.3511925'
apa: 'Heindorf, S., Blübaum, L., Düsterhus, N., Werner, T., Golani, V. N., Demir,
C., & Ngonga Ngomo, A.-C. (2022). EvoLearner: Learning Description Logics
with Evolutionary Algorithms. WWW, 818–828. https://doi.org/10.1145/3485447.3511925'
bibtex: '@inproceedings{Heindorf_Blübaum_Düsterhus_Werner_Golani_Demir_Ngonga Ngomo_2022,
title={EvoLearner: Learning Description Logics with Evolutionary Algorithms},
DOI={10.1145/3485447.3511925},
booktitle={WWW}, publisher={ACM}, author={Heindorf, Stefan and Blübaum, Lukas
and Düsterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar
and Ngonga Ngomo, Axel-Cyrille}, year={2022}, pages={818–828} }'
chicago: 'Heindorf, Stefan, Lukas Blübaum, Nick Düsterhus, Till Werner, Varun Nandkumar
Golani, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “EvoLearner: Learning Description
Logics with Evolutionary Algorithms.” In WWW, 818–28. ACM, 2022. https://doi.org/10.1145/3485447.3511925.'
ieee: 'S. Heindorf et al., “EvoLearner: Learning Description Logics with
Evolutionary Algorithms,” in WWW, 2022, pp. 818–828, doi: 10.1145/3485447.3511925.'
mla: 'Heindorf, Stefan, et al. “EvoLearner: Learning Description Logics with Evolutionary
Algorithms.” WWW, ACM, 2022, pp. 818–28, doi:10.1145/3485447.3511925.'
short: 'S. Heindorf, L. Blübaum, N. Düsterhus, T. Werner, V.N. Golani, C. Demir,
A.-C. Ngonga Ngomo, in: WWW, ACM, 2022, pp. 818–828.'
date_created: 2022-01-12T10:22:53Z
date_updated: 2024-05-26T19:13:09Z
department:
- _id: '574'
doi: 10.1145/3485447.3511925
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2111.04879
oa: '1'
page: 818-828
publication: WWW
publisher: ACM
status: public
title: 'EvoLearner: Learning Description Logics with Evolutionary Algorithms'
type: conference
user_id: '11871'
year: '2022'
...
---
_id: '25206'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Demir C, Ngonga Ngomo A-C. Convolutional Complex Knowledge Graph Embeddings.
In: Verborgh R, Hose K, Paulheim H, et al., eds. The Semantic Web - 18th International
Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021, Proceedings. Vol
12731. Lecture Notes in Computer Science. Springer; 2021:409-424. doi:10.1007/978-3-030-77385-4\_24'
apa: Demir, C., & Ngonga Ngomo, A.-C. (2021). Convolutional Complex Knowledge
Graph Embeddings. In R. Verborgh, K. Hose, H. Paulheim, P.-}Antoine Champin, M.
Maleshkova, O. Corcho, P. Ristoski, & M. Alam (Eds.), The Semantic Web
- 18th International Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021,
Proceedings (Vol. 12731, pp. 409–424). Springer. https://doi.org/10.1007/978-3-030-77385-4\_24
bibtex: '@inproceedings{Demir_Ngonga Ngomo_2021, series={Lecture Notes in Computer
Science}, title={Convolutional Complex Knowledge Graph Embeddings}, volume={12731},
DOI={10.1007/978-3-030-77385-4\_24},
booktitle={The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual
Event, June 6-10, 2021, Proceedings}, publisher={Springer}, author={Demir, Caglar
and Ngonga Ngomo, Axel-Cyrille}, editor={Verborgh, Ruben and Hose, Katja and Paulheim,
Heiko and Champin, Pierre{-}Antoine and Maleshkova, Maria and Corcho, Oscar and
Ristoski, Petar and Alam, Mehwish}, year={2021}, pages={409–424}, collection={Lecture
Notes in Computer Science} }'
chicago: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Convolutional Complex Knowledge
Graph Embeddings.” In The Semantic Web - 18th International Conference, {ESWC}
2021, Virtual Event, June 6-10, 2021, Proceedings, edited by Ruben Verborgh,
Katja Hose, Heiko Paulheim, Pierre{-}Antoine Champin, Maria Maleshkova, Oscar
Corcho, Petar Ristoski, and Mehwish Alam, 12731:409–24. Lecture Notes in Computer
Science. Springer, 2021. https://doi.org/10.1007/978-3-030-77385-4\_24.
ieee: 'C. Demir and A.-C. Ngonga Ngomo, “Convolutional Complex Knowledge Graph Embeddings,”
in The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual Event,
June 6-10, 2021, Proceedings, 2021, vol. 12731, pp. 409–424, doi: 10.1007/978-3-030-77385-4\_24.'
mla: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Convolutional Complex Knowledge
Graph Embeddings.” The Semantic Web - 18th International Conference, {ESWC}
2021, Virtual Event, June 6-10, 2021, Proceedings, edited by Ruben Verborgh
et al., vol. 12731, Springer, 2021, pp. 409–24, doi:10.1007/978-3-030-77385-4\_24.
short: 'C. Demir, A.-C. Ngonga Ngomo, in: R. Verborgh, K. Hose, H. Paulheim, P.-}Antoine
Champin, M. Maleshkova, O. Corcho, P. Ristoski, M. Alam (Eds.), The Semantic Web
- 18th International Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021,
Proceedings, Springer, 2021, pp. 409–424.'
date_created: 2021-10-01T06:45:57Z
date_updated: 2022-01-06T06:56:55Z
department:
- _id: '574'
doi: 10.1007/978-3-030-77385-4\_24
editor:
- first_name: Ruben
full_name: Verborgh, Ruben
last_name: Verborgh
- first_name: Katja
full_name: Hose, Katja
last_name: Hose
- first_name: Heiko
full_name: Paulheim, Heiko
last_name: Paulheim
- first_name: Pierre{-}Antoine
full_name: Champin, Pierre{-}Antoine
last_name: Champin
- first_name: Maria
full_name: Maleshkova, Maria
last_name: Maleshkova
- first_name: Oscar
full_name: Corcho, Oscar
last_name: Corcho
- first_name: Petar
full_name: Ristoski, Petar
last_name: Ristoski
- first_name: Mehwish
full_name: Alam, Mehwish
last_name: Alam
intvolume: ' 12731'
language:
- iso: eng
page: 409-424
publication: The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual
Event, June 6-10, 2021, Proceedings
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: Convolutional Complex Knowledge Graph Embeddings
type: conference
user_id: '65716'
volume: 12731
year: '2021'
...