---
_id: '62701'
abstract:
- lang: eng
text: 'Learning continuous vector representations for knowledge graphs has
significantly improved state-of-the-art performances in many challenging tasks.
Yet, deep-learning-based models are only post-hoc and locally explainable. In
contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally
explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn
Web Ontology Languages (OWLs) class expressions over large knowl-edge
graphs, while imputing missing triples. Given positive and negative example individuals,
tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL
class expression is used as a feature in a binary classification problem to represent
input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean
decision rules distinguishing positive examples from nega-tive examples. A final
OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each
positive example. By this, tDL can learn OWL class expressions without exploration,
i.e., the number of queries to a knowledge graph is bounded by the number of input
individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across
datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia
with 1.1 billion triples) show that tDL can effectively learn accurate OWL class
expressions, while the state-of-the-art models fail to return any results.
Finally, expressions learned by tDL can be seamlessly translated into
natural language explanations using a pre-trained large language model and a DL
verbalizer.'
author:
- first_name: Caglar
full_name: Demir, Caglar
last_name: Demir
- first_name: Moshood
full_name: Yekini, Moshood
last_name: Yekini
- first_name: Michael
full_name: Röder, Michael
last_name: Röder
- first_name: Yasir
full_name: Mahmood, Yasir
last_name: Mahmood
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: 'Demir C, Yekini M, 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., Röder, M., Mahmood, Y., & Ngonga Ngomo, A.-C. (2025).
Tree-Based OWL Class Expression Learner over Large Graphs. In Lecture Notes
in Computer Science. European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-06066-2_29
bibtex: '@inbook{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 and Röder, Michael and Mahmood, Yasir
and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Demir, Caglar, Moshood 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. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based
OWL Class Expression Learner over Large Graphs,” in Lecture Notes in Computer
Science, Cham: Springer Nature Switzerland, 2025.'
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. 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, Portugal
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases - ECML PKDD
start_date: 2025-09-15
date_created: 2025-11-28T14:09:17Z
date_updated: 2025-11-28T14:57:39Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-032-06066-2_29
keyword:
- Decision Tree
- OWL Class Expression Learning
- Description Logic
- Knowledge Graph
- Large Language Model
- Verbalizer
language:
- iso: eng
place: Cham
project:
- _id: '285'
name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
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: book_chapter
user_id: '114533'
year: '2025'
...
---
_id: '61041'
abstract:
- lang: eng
text: Large Language Models (LLMs) are increasingly deployed in real-world applications
that require access to up-to-date knowledge. However, retraining LLMs is computationally
expensive. Therefore, knowledge editing techniques are crucial for maintaining
current information and correcting erroneous assertions within pre-trained models.
Current benchmarks for knowledge editing primarily focus on recalling edited facts,
often neglecting their logical consequences. To address this limitation, we introduce
a new benchmark designed to evaluate how knowledge editing methods handle the
logical consequences of a single fact edit. Our benchmark extracts relevant logical
rules from a knowledge graph for a given edit. Then, it generates multi-hop questions
based on these rules to assess the impact on logical consequences. Our findings
indicate that while existing knowledge editing approaches can accurately insert
direct assertions into LLMs, they frequently fail to inject entailed knowledge.
Specifically, experiments with popular methods like ROME and FT reveal a substantial
performance gap, up to 24%, between evaluations on directly edited knowledge and
on entailed knowledge. This highlights the critical need for semantics-aware evaluation
frameworks in knowledge editing.
author:
- first_name: Tatiana
full_name: Moteu Ngoli, Tatiana
id: '99174'
last_name: Moteu Ngoli
- first_name: N'Dah Jean
full_name: Kouagou, N'Dah Jean
id: '87189'
last_name: Kouagou
- first_name: Hamada Mohamed Abdelsamee
full_name: Zahera, Hamada Mohamed Abdelsamee
id: '72768'
last_name: Zahera
orcid: 0000-0003-0215-1278
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Moteu Ngoli T, Kouagou NJ, Zahera HMA, Ngonga Ngomo A-C. Benchmarking Knowledge
Editing using Logical Rules. In: Proceedings of the 24th International Semantic
Web Conference (ISWC 2025). Springer, Cham; 2025:pp 41-56. doi:https://doi.org/10.1007/978-3-032-09530-5_3'
apa: Moteu Ngoli, T., Kouagou, N. J., Zahera, H. M. A., & Ngonga Ngomo, A.-C.
(2025). Benchmarking Knowledge Editing using Logical Rules. Proceedings of
the 24th International Semantic Web Conference (ISWC 2025), pp 41-56. https://doi.org/10.1007/978-3-032-09530-5_3
bibtex: '@inproceedings{Moteu Ngoli_Kouagou_Zahera_Ngonga Ngomo_2025, title={Benchmarking
Knowledge Editing using Logical Rules}, DOI={https://doi.org/10.1007/978-3-032-09530-5_3},
booktitle={Proceedings of the 24th International Semantic Web Conference (ISWC
2025)}, publisher={Springer, Cham}, author={Moteu Ngoli, Tatiana and Kouagou,
N’Dah Jean and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille},
year={2025}, pages={pp 41-56} }'
chicago: Moteu Ngoli, Tatiana, N’Dah Jean Kouagou, Hamada Mohamed Abdelsamee Zahera,
and Axel-Cyrille Ngonga Ngomo. “Benchmarking Knowledge Editing Using Logical Rules.”
In Proceedings of the 24th International Semantic Web Conference (ISWC 2025),
pp 41-56. Springer, Cham, 2025. https://doi.org/10.1007/978-3-032-09530-5_3.
ieee: 'T. Moteu Ngoli, N. J. Kouagou, H. M. A. Zahera, and A.-C. Ngonga Ngomo, “Benchmarking
Knowledge Editing using Logical Rules,” in Proceedings of the 24th International
Semantic Web Conference (ISWC 2025), Nara, Japan, 2025, p. pp 41-56, doi:
https://doi.org/10.1007/978-3-032-09530-5_3.'
mla: Moteu Ngoli, Tatiana, et al. “Benchmarking Knowledge Editing Using Logical
Rules.” Proceedings of the 24th International Semantic Web Conference (ISWC
2025), Springer, Cham, 2025, p. pp 41-56, doi:https://doi.org/10.1007/978-3-032-09530-5_3.
short: 'T. Moteu Ngoli, N.J. Kouagou, H.M.A. Zahera, A.-C. Ngonga Ngomo, in: Proceedings
of the 24th International Semantic Web Conference (ISWC 2025), Springer, Cham,
2025, p. pp 41-56.'
conference:
end_date: 2025.11.6
location: Nara, Japan
name: The 24th International Semantic Web Conference (ISWC 2025)
start_date: 2025.11.2
date_created: 2025-08-27T13:17:55Z
date_updated: 2025-12-01T10:04:25Z
department:
- _id: '574'
doi: https://doi.org/10.1007/978-3-032-09530-5_3
keyword:
- dice sailproject moteu kouagou zahera ngonga
language:
- iso: eng
page: pp 41-56
project:
- _id: '285'
name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Proceedings of the 24th International Semantic Web Conference (ISWC 2025)
publication_identifier:
isbn:
- 978-3-032-09530-5
publication_status: published
publisher: Springer, Cham
status: public
title: Benchmarking Knowledge Editing using Logical Rules
type: conference
user_id: '99174'
year: '2025'
...
---
_id: '62007'
abstract:
- lang: eng
text: "Ensemble methods are widely employed to improve generalization in machine
learning. This has also prompted the adoption of ensemble learning for the knowledge
graph embedding (KGE) models in performing link prediction. Typical approaches
to this end train multiple models as part of the ensemble, and the diverse predictions
are then averaged. However, this approach has some significant drawbacks. For
instance, the computational overhead of training multiple models increases latency
and memory overhead. In contrast, model merging approaches offer a promising alternative
that does not require training multiple models. In this work, we introduce model
merging, specifically weighted averaging, in\r\nKGE models. Herein, a running
average of model parameters from a training epoch onward is maintained and used
for predictions. To address this, we additionally propose an approach that selectively
updates the running average of the ensemble model parameters only when the generalization
performance improves on a validation dataset. We evaluate these two different
weighted averaging approaches on link prediction tasks, comparing the state-of-the-art
benchmark ensemble approach. Additionally, we evaluate the weighted averaging
approach considering literal-augmented KGE models and multi-hop query answering
tasks as well. The results demonstrate that the proposed weighted averaging approach
consistently improves performance across diverse evaluation settings."
author:
- first_name: Rupesh
full_name: Sapkota, Rupesh
id: '89326'
last_name: Sapkota
- first_name: Caglar
full_name: Demir, Caglar
last_name: Demir
- first_name: Arnab
full_name: Sharma, Arnab
last_name: Sharma
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: 'Sapkota R, Demir C, Sharma A, Ngonga Ngomo A-C. Parameter Averaging in Link
Prediction. In: Proceedings of the Thirteenth International Conference on Knowledge
Capture(K-CAP 2025). ACM; 2025. doi:https://doi.org/10.1145/3731443.3771365'
apa: Sapkota, R., Demir, C., Sharma, A., & Ngonga Ngomo, A.-C. (2025). Parameter
Averaging in Link Prediction. Proceedings of the Thirteenth International Conference
on Knowledge Capture(K-CAP 2025). Knowledge Capture Conference 2025, Dayton,
OH, USA. https://doi.org/10.1145/3731443.3771365
bibtex: '@inproceedings{Sapkota_Demir_Sharma_Ngonga Ngomo_2025, place={Dayton, OH,
USA}, title={Parameter Averaging in Link Prediction}, DOI={https://doi.org/10.1145/3731443.3771365},
booktitle={Proceedings of the Thirteenth International Conference on Knowledge
Capture(K-CAP 2025)}, publisher={ACM}, author={Sapkota, Rupesh and Demir, Caglar
and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: 'Sapkota, Rupesh, Caglar Demir, Arnab Sharma, and Axel-Cyrille Ngonga Ngomo.
“Parameter Averaging in Link Prediction.” In Proceedings of the Thirteenth
International Conference on Knowledge Capture(K-CAP 2025). Dayton, OH, USA:
ACM, 2025. https://doi.org/10.1145/3731443.3771365.'
ieee: 'R. Sapkota, C. Demir, A. Sharma, and A.-C. Ngonga Ngomo, “Parameter Averaging
in Link Prediction,” presented at the Knowledge Capture Conference 2025, Dayton,
OH, USA, 2025, doi: https://doi.org/10.1145/3731443.3771365.'
mla: Sapkota, Rupesh, et al. “Parameter Averaging in Link Prediction.” Proceedings
of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025),
ACM, 2025, doi:https://doi.org/10.1145/3731443.3771365.
short: 'R. Sapkota, C. Demir, A. Sharma, A.-C. Ngonga Ngomo, in: Proceedings of
the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM,
Dayton, OH, USA, 2025.'
conference:
end_date: 2025-12-10
location: Dayton, OH, USA
name: Knowledge Capture Conference 2025
start_date: 2025-12-10
date_created: 2025-10-28T10:02:40Z
date_updated: 2025-12-04T09:15:07Z
ddc:
- '000'
department:
- _id: '574'
doi: https://doi.org/10.1145/3731443.3771365
file:
- access_level: open_access
content_type: application/pdf
creator: rupezzz
date_created: 2025-10-28T10:02:13Z
date_updated: 2025-10-28T10:02:13Z
file_id: '62008'
file_name: public.pdf
file_size: 837462
relation: main_file
file_date_updated: 2025-10-28T10:02:13Z
has_accepted_license: '1'
keyword:
- Knowledge Graphs
- Embeddings
- Ensemble Learning
language:
- iso: eng
main_file_link:
- url: https://papers.dice-research.org/2025/KCAP_ASWA/public.pdf
oa: '1'
place: Dayton, OH, USA
project:
- _id: '285'
name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP
2025)
publisher: ACM
status: public
title: Parameter Averaging in Link Prediction
type: conference
user_id: '89326'
year: '2025'
...
---
_id: '56213'
author:
- first_name: Rupesh
full_name: Sapkota, Rupesh
id: '89326'
last_name: Sapkota
- first_name: Dominik
full_name: Köhler, Dominik
last_name: Köhler
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
citation:
ama: 'Sapkota R, Köhler D, Heindorf S. EDGE: Evaluation Framework for Logical vs.
Subgraph Explanations for Node Classifiers on Knowledge Graphs. In: Proceedings
of the 33rd ACM International Conference on Information and Knowledge Management
(CIKM ’24),. ACM; 2024. doi:10.1145/3627673.3679904'
apa: 'Sapkota, R., Köhler, D., & Heindorf, S. (2024). EDGE: Evaluation Framework
for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs.
Proceedings of the 33rd ACM International Conference on Information and Knowledge
Management (CIKM ’24),. 33rd ACM International Conference on Information and
Knowledge Management, Boise, Idaho, USA. https://doi.org/10.1145/3627673.3679904'
bibtex: '@inproceedings{Sapkota_Köhler_Heindorf_2024, place={Boise, Idaho, USA},
title={EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node
Classifiers on Knowledge Graphs}, DOI={10.1145/3627673.3679904},
booktitle={Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management (CIKM ’24),}, publisher={ACM}, author={Sapkota, Rupesh
and Köhler, Dominik and Heindorf, Stefan}, year={2024} }'
chicago: 'Sapkota, Rupesh, Dominik Köhler, and Stefan Heindorf. “EDGE: Evaluation
Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge
Graphs.” In Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management (CIKM ’24),. Boise, Idaho, USA: ACM, 2024. https://doi.org/10.1145/3627673.3679904.'
ieee: 'R. Sapkota, D. Köhler, and S. Heindorf, “EDGE: Evaluation Framework for Logical
vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs,” presented
at the 33rd ACM International Conference on Information and Knowledge Management,
Boise, Idaho, USA, 2024, doi: 10.1145/3627673.3679904.'
mla: 'Sapkota, Rupesh, et al. “EDGE: Evaluation Framework for Logical vs. Subgraph
Explanations for Node Classifiers on Knowledge Graphs.” Proceedings of the
33rd ACM International Conference on Information and Knowledge Management (CIKM
’24), ACM, 2024, doi:10.1145/3627673.3679904.'
short: 'R. Sapkota, D. Köhler, S. Heindorf, in: Proceedings of the 33rd ACM International
Conference on Information and Knowledge Management (CIKM ’24), ACM, Boise, Idaho,
USA, 2024.'
conference:
end_date: 2024-10-25
location: Boise, Idaho, USA
name: 33rd ACM International Conference on Information and Knowledge Management
start_date: 2024-10-21
date_created: 2024-09-23T12:30:10Z
date_updated: 2024-09-23T12:30:25Z
department:
- _id: '760'
- _id: '574'
doi: 10.1145/3627673.3679904
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2024/CIKM_EDGE/public.pdf
oa: '1'
place: Boise, Idaho, USA
project:
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen
Systemen'
publication: Proceedings of the 33rd ACM International Conference on Information and
Knowledge Management (CIKM ’24),
publisher: ACM
status: public
title: 'EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node
Classifiers on Knowledge Graphs'
type: conference
user_id: '11871'
year: '2024'
...
---
_id: '57240'
abstract:
- lang: eng
text: Validating assertions before adding them to a knowledge graph is an essential
part of its creation and maintenance. Due to the sheer size of knowledge graphs,
automatic fact-checking approaches have been developed. These approaches rely
on reference knowledge to decide whether a given assertion is correct. Recent
hybrid approaches achieve good results by including several knowledge sources.
However, it is often impractical to provide a sheer quantity of textual knowledge
or generate embedding models to leverage these hybrid approaches. We present FaVEL,
an approach that uses algorithm selection and ensemble learning to amalgamate
several existing fact-checking approaches that rely solely on a reference knowledge
graph and, hence, use fewer resources than current hybrid approaches. For our
evaluation, we create updated versions of two existing datasets and a new dataset
dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including
the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate
that FaVEL outperforms all other approaches significantly by at least 0.04 in
terms of the area under the ROC curve. Our source code, datasets, and evaluation
results are open-source and can be found at https://github.com/dice-group/favel.
author:
- first_name: Umair
full_name: Qudus, Umair
id: '83392'
last_name: Qudus
orcid: 0000-0001-6714-8729
- first_name: Michael
full_name: Röder, Michael
id: '67199'
last_name: Röder
orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Franck Lionel
full_name: Tatkeu Pekarou, Franck Lionel
last_name: Tatkeu Pekarou
- first_name: Ana Alexandra
full_name: Morim da Silva, Ana Alexandra
id: '72108'
last_name: Morim da Silva
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Qudus U, Röder M, Tatkeu Pekarou FL, Morim da Silva AA, Ngonga Ngomo A-C.
FaVEL: Fact Validation Ensemble Learning. In: Rospocher M, Mehwish Alam, eds.
EKAW 2024. ; 2024.'
apa: 'Qudus, U., Röder, M., Tatkeu Pekarou, F. L., Morim da Silva, A. A., &
Ngonga Ngomo, A.-C. (2024). FaVEL: Fact Validation Ensemble Learning. In M. Rospocher
& Mehwish Alam (Eds.), EKAW 2024.'
bibtex: '@inproceedings{Qudus_Röder_Tatkeu Pekarou_Morim da Silva_Ngonga Ngomo_2024,
title={FaVEL: Fact Validation Ensemble Learning}, booktitle={EKAW 2024}, author={Qudus,
Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva,
Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}, editor={Rospocher, Marco and Mehwish
Alam}, year={2024} }'
chicago: 'Qudus, Umair, Michael Röder, Franck Lionel Tatkeu Pekarou, Ana Alexandra
Morim da Silva, and Axel-Cyrille Ngonga Ngomo. “FaVEL: Fact Validation Ensemble
Learning.” In EKAW 2024, edited by Marco Rospocher and Mehwish Alam, 2024.'
ieee: 'U. Qudus, M. Röder, F. L. Tatkeu Pekarou, A. A. Morim da Silva, and A.-C.
Ngonga Ngomo, “FaVEL: Fact Validation Ensemble Learning,” in EKAW 2024,
Amsterdam, Netherlands, 2024.'
mla: 'Qudus, Umair, et al. “FaVEL: Fact Validation Ensemble Learning.” EKAW 2024,
edited by Marco Rospocher and Mehwish Alam, 2024.'
short: 'U. Qudus, M. Röder, F.L. Tatkeu Pekarou, A.A. Morim da Silva, A.-C. Ngonga
Ngomo, in: M. Rospocher, Mehwish Alam (Eds.), EKAW 2024, 2024.'
conference:
end_date: 2024-11-28
location: Amsterdam, Netherlands
name: 24th International Conference on Knowledge Engineering and Knowledge Management
start_date: 2024-11-26
corporate_editor:
- Mehwish Alam
date_created: 2024-11-19T14:12:49Z
date_updated: 2025-09-11T09:48:12Z
ddc:
- '600'
department:
- _id: '34'
editor:
- first_name: Marco
full_name: Rospocher, Marco
last_name: Rospocher
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-11-19T14:14:14Z
date_updated: 2024-11-19T14:14:14Z
file_id: '57241'
file_name: favel.pdf
file_size: 190661
relation: main_file
success: 1
file_date_updated: 2024-11-19T14:14:14Z
has_accepted_license: '1'
keyword:
- fact checking
- ensemble learning
- transfer learning
- knowledge management.
language:
- iso: eng
popular_science: '1'
project:
- _id: '412'
name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen'
- _id: '285'
name: 'SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen
Systemen'
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: EKAW 2024
quality_controlled: '1'
status: public
title: 'FaVEL: Fact Validation Ensemble Learning'
type: conference
user_id: '83392'
year: '2024'
...
---
_id: '61210'
abstract:
- lang: eng
text: Knowledge graphs (KGs) differ significantly over multiple different versions
of the same data source. They also often contain blank nodes that do not have
a constant identifier over all versions. Linking such blank nodes from different
versions is a challenging task. Previous works propose different approaches to
create signatures for all blank nodes based on named nodes in their neighborhood
to match blank nodes with similar signatures. However, these works struggle to
find a good mapping when the difference between the KGs’ versions grows too large.
In this work, we propose Blink, an embedding-based approach for blank node linking.
Blink merges two KGs’ versions and embeds the merged graph into a latent vector
space based on translational embeddings and subsequently matches the closest pairs
of blank nodes from different graphs. We evaluate our approach using real-world
datasets against state-of-the-art approaches by computing the blank node matching
for isomorphic graphs and graphs that contain triple changes (i.e., added or removed
triples). The results indicate that Blink achieves perfect accuracy for isomorphic
graphs. For graph versions that contain changes, such as having up to 20% of triples
removed in one version, Blink still produces a mapping with an Optimal Mapping
Deviation Ratio of under 1%. These results show that Blink leads to a better linking
of KGs over different versions and similar graphs adhering to the linked data
guidelines.
author:
- first_name: Alexander
full_name: Becker, Alexander
last_name: Becker
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Becker A, Sherif M, Ngonga Ngomo A-C. Blink: Blank Node Matching Using Embeddings.
In: Lecture Notes in Computer Science. Springer Nature Switzerland; 2024.
doi:10.1007/978-3-031-77844-5_12'
apa: 'Becker, A., Sherif, M., & Ngonga Ngomo, A.-C. (2024). Blink: Blank Node
Matching Using Embeddings. In Lecture Notes in Computer Science. ISWC 2024: :
The 23ed International Semantic Web Conference, Baltimore, USA. Springer Nature
Switzerland. https://doi.org/10.1007/978-3-031-77844-5_12'
bibtex: '@inbook{Becker_Sherif_Ngonga Ngomo_2024, place={Cham}, title={Blink: Blank
Node Matching Using Embeddings}, DOI={10.1007/978-3-031-77844-5_12},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Becker, Alexander and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille},
year={2024} }'
chicago: 'Becker, Alexander, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Blink:
Blank Node Matching Using Embeddings.” In Lecture Notes in Computer Science.
Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77844-5_12.'
ieee: 'A. Becker, M. Sherif, and A.-C. Ngonga Ngomo, “Blink: Blank Node Matching
Using Embeddings,” in Lecture Notes in Computer Science, Cham: Springer
Nature Switzerland, 2024.'
mla: 'Becker, Alexander, et al. “Blink: Blank Node Matching Using Embeddings.” Lecture
Notes in Computer Science, Springer Nature Switzerland, 2024, doi:10.1007/978-3-031-77844-5_12.'
short: 'A. Becker, M. Sherif, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer
Science, Springer Nature Switzerland, Cham, 2024.'
conference:
end_date: 2024-11-15
location: Baltimore, USA
name: 'ISWC 2024: : The 23ed International Semantic Web Conference'
start_date: 2024-11-11
date_created: 2025-09-11T10:19:47Z
date_updated: 2025-09-11T10:34:02Z
department:
- _id: '574'
doi: 10.1007/978-3-031-77844-5_12
language:
- iso: eng
main_file_link:
- url: https://papers.dice-research.org/2024/ISWC_BLINK/public.pdf
place: Cham
project:
- _id: '285'
name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783031778438'
- '9783031778445'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: 'Blink: Blank Node Matching Using Embeddings'
type: book_chapter
user_id: '67234'
year: '2024'
...
---
_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: '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'
...