---
_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: '35602'
abstract:
- lang: eng
text: "Continuous Speech Separation (CSS) has been proposed to address speech overlaps
during the analysis of realistic meeting-like conversations by eliminating any
overlaps before further processing.\r\nCSS separates a recording of arbitrarily
many speakers into a small number of overlap-free output channels, where each
output channel may contain speech of multiple speakers.\r\nThis is often done
by applying a conventional separation model trained with Utterance-level Permutation
Invariant Training (uPIT), which exclusively maps a speaker to an output channel,
in sliding window approach called stitching.\r\nRecently, we introduced an alternative
training scheme called Graph-PIT that teaches the separation network to directly
produce output streams in the required format without stitching.\r\nIt can handle
an arbitrary number of speakers as long as never more of them overlap at the same
time than the separator has output channels.\r\nIn this contribution, we further
investigate the Graph-PIT training scheme.\r\nWe show in extended experiments
that models trained with Graph-PIT also work in challenging reverberant conditions.\r\nModels
trained in this way are able to perform segment-less CSS, i.e., without stitching,
and achieve comparable and often better separation quality than the conventional
CSS with uPIT and stitching.\r\nWe simplify the training schedule for Graph-PIT
with the recently proposed Source Aggregated Signal-to-Distortion Ratio (SA-SDR)
loss.\r\nIt eliminates unfavorable properties of the previously used A-SDR loss
and thus enables training with Graph-PIT from scratch.\r\nGraph-PIT training relaxes
the constraints w.r.t. the allowed numbers of speakers and speaking patterns which
allows using a larger variety of training data.\r\nFurthermore, we introduce novel
signal-level evaluation metrics for meeting scenarios, namely the source-aggregated
scale- and convolution-invariant Signal-to-Distortion Ratio (SA-SI-SDR and SA-CI-SDR),
which are generalizations of the commonly used SDR-based metrics for the CSS case."
article_type: original
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Kinoshita K, Boeddeker C, Delcroix M, Haeb-Umbach R. Segment-Less
Continuous Speech Separation of Meetings: Training and Evaluation Criteria. IEEE/ACM
Transactions on Audio, Speech, and Language Processing. 2023;31:576-589. doi:10.1109/taslp.2022.3228629'
apa: 'von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., & Haeb-Umbach,
R. (2023). Segment-Less Continuous Speech Separation of Meetings: Training and
Evaluation Criteria. IEEE/ACM Transactions on Audio, Speech, and Language Processing,
31, 576–589. https://doi.org/10.1109/taslp.2022.3228629'
bibtex: '@article{von Neumann_Kinoshita_Boeddeker_Delcroix_Haeb-Umbach_2023, title={Segment-Less
Continuous Speech Separation of Meetings: Training and Evaluation Criteria}, volume={31},
DOI={10.1109/taslp.2022.3228629},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, publisher={Institute
of Electrical and Electronics Engineers (IEEE)}, author={von Neumann, Thilo and
Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach,
Reinhold}, year={2023}, pages={576–589} }'
chicago: 'Neumann, Thilo von, Keisuke Kinoshita, Christoph Boeddeker, Marc Delcroix,
and Reinhold Haeb-Umbach. “Segment-Less Continuous Speech Separation of Meetings:
Training and Evaluation Criteria.” IEEE/ACM Transactions on Audio, Speech,
and Language Processing 31 (2023): 576–89. https://doi.org/10.1109/taslp.2022.3228629.'
ieee: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach,
“Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation
Criteria,” IEEE/ACM Transactions on Audio, Speech, and Language Processing,
vol. 31, pp. 576–589, 2023, doi: 10.1109/taslp.2022.3228629.'
mla: 'von Neumann, Thilo, et al. “Segment-Less Continuous Speech Separation of Meetings:
Training and Evaluation Criteria.” IEEE/ACM Transactions on Audio, Speech,
and Language Processing, vol. 31, Institute of Electrical and Electronics
Engineers (IEEE), 2023, pp. 576–89, doi:10.1109/taslp.2022.3228629.'
short: T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach,
IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2023) 576–589.
date_created: 2023-01-09T17:24:17Z
date_updated: 2023-11-15T12:16:11Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/taslp.2022.3228629
file:
- access_level: open_access
content_type: application/pdf
creator: haebumb
date_created: 2023-01-09T17:46:05Z
date_updated: 2023-01-11T08:50:19Z
file_id: '35607'
file_name: main.pdf
file_size: 7185077
relation: main_file
file_date_updated: 2023-01-11T08:50:19Z
has_accepted_license: '1'
intvolume: ' 31'
keyword:
- Continuous Speech Separation
- Source Separation
- Graph-PIT
- Dynamic Programming
- Permutation Invariant Training
language:
- iso: eng
oa: '1'
page: 576-589
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
publication_identifier:
issn:
- 2329-9290
- 2329-9304
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
quality_controlled: '1'
status: public
title: 'Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation
Criteria'
type: journal_article
user_id: '49870'
volume: 31
year: '2023'
...
---
_id: '44390'
abstract:
- lang: eng
text: The development of autonomous vehicles and their introduction in urban traffic
offer many opportunities for traffic improvements. In this paper, an approach
for a future traffic control system for mixed autonomy traffic environments is
presented. Furthermore, a simulation framework based on the city of Paderborn
is introduced to enable the development and examination of such a system. This
encompasses multiple elements including the road network itself, traffic lights,
sensors as well as methods to analyse the topology of the network. Furthermore,
a procedure for traffic demand generation and routing is presented based on statistical
data of the city and traffic data obtained by measurements. The resulting model
can receive and apply the generated control inputs and in turn generates simulated
sensor data for the control system based on the current system state.
author:
- first_name: Christopher
full_name: Link, Christopher
id: '38249'
last_name: Link
- first_name: Kevin
full_name: Malena, Kevin
id: '36303'
last_name: Malena
orcid: 0000-0003-1183-4679
- first_name: Sandra
full_name: Gausemeier, Sandra
id: '17793'
last_name: Gausemeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Link C, Malena K, Gausemeier S, Trächtler A. Simulation Environment for Traffic
Control Systems Targeting Mixed Autonomy Traffic Scenarios. In: Proceedings
of the 9th International Conference on Vehicle Technology and Intelligent Transport
Systems. SCITEPRESS - Science and Technology Publications; 2023. doi:10.5220/0011987600003479'
apa: Link, C., Malena, K., Gausemeier, S., & Trächtler, A. (2023). Simulation
Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios.
Proceedings of the 9th International Conference on Vehicle Technology and Intelligent
Transport Systems. 9th International Conference on Vehicle Technology and
Intelligent Transport Systems (VEHITS 2023), Prague, Czech Republic. https://doi.org/10.5220/0011987600003479
bibtex: '@inproceedings{Link_Malena_Gausemeier_Trächtler_2023, title={Simulation
Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios},
DOI={10.5220/0011987600003479},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology
and Intelligent Transport Systems}, publisher={SCITEPRESS - Science and Technology
Publications}, author={Link, Christopher and Malena, Kevin and Gausemeier, Sandra
and Trächtler, Ansgar}, year={2023} }'
chicago: Link, Christopher, Kevin Malena, Sandra Gausemeier, and Ansgar Trächtler.
“Simulation Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic
Scenarios.” In Proceedings of the 9th International Conference on Vehicle Technology
and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications,
2023. https://doi.org/10.5220/0011987600003479.
ieee: 'C. Link, K. Malena, S. Gausemeier, and A. Trächtler, “Simulation Environment
for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios,” presented
at the 9th International Conference on Vehicle Technology and Intelligent Transport
Systems (VEHITS 2023), Prague, Czech Republic, 2023, doi: 10.5220/0011987600003479.'
mla: Link, Christopher, et al. “Simulation Environment for Traffic Control Systems
Targeting Mixed Autonomy Traffic Scenarios.” Proceedings of the 9th International
Conference on Vehicle Technology and Intelligent Transport Systems, SCITEPRESS
- Science and Technology Publications, 2023, doi:10.5220/0011987600003479.
short: 'C. Link, K. Malena, S. Gausemeier, A. Trächtler, in: Proceedings of the
9th International Conference on Vehicle Technology and Intelligent Transport Systems,
SCITEPRESS - Science and Technology Publications, 2023.'
conference:
end_date: 2023-04-28
location: Prague, Czech Republic
name: 9th International Conference on Vehicle Technology and Intelligent Transport
Systems (VEHITS 2023)
start_date: 2023-04-26
date_created: 2023-05-03T08:57:10Z
date_updated: 2023-05-03T09:15:19Z
department:
- _id: '153'
doi: 10.5220/0011987600003479
keyword:
- Traffic Simulation
- Traffic Control
- Car2X
- Mixed Autonomy
- Autonomous Vehicles
- SUMO
- Sensor Simulation
- Traffic Demand Generation
- Routing
- Traffic Lights
- Graph Analysis
- Traffic Observer
language:
- iso: eng
main_file_link:
- url: https://www.scitepress.org/Link.aspx?doi=10.5220/0011987600003479
publication: Proceedings of the 9th International Conference on Vehicle Technology
and Intelligent Transport Systems
publication_identifier:
isbn:
- 978-989-758-652-1
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
quality_controlled: '1'
status: public
title: Simulation Environment for Traffic Control Systems Targeting Mixed Autonomy
Traffic Scenarios
type: conference
user_id: '38249'
year: '2023'
...
---
_id: '33957'
abstract:
- lang: eng
text: Manufacturing companies are challenged to make the increasingly complex work
processes equally manageable for all employees to prevent an impending loss of
competence. In this contribution, an intelligent assistance system is proposed
enabling employees to help themselves in the workplace and provide them with competence-related
support. This results in increasing the short- and long-term efficiency of problem
solving in companies.
author:
- first_name: Sahar
full_name: Deppe, Sahar
last_name: Deppe
- first_name: Lukas
full_name: Brandt, Lukas
last_name: Brandt
- first_name: Marc
full_name: Brünninghaus, Marc
last_name: Brünninghaus
- first_name: Jörg
full_name: Papenkordt, Jörg
id: '44648'
last_name: Papenkordt
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Gudrun
full_name: Tschirner-Vinke, Gudrun
last_name: Tschirner-Vinke
citation:
ama: Deppe S, Brandt L, Brünninghaus M, Papenkordt J, Heindorf S, Tschirner-Vinke
G. AI-Based Assistance System for Manufacturing. Published online 2022. doi:10.1109/ETFA52439.2022.9921520
apa: Deppe, S., Brandt, L., Brünninghaus, M., Papenkordt, J., Heindorf, S., &
Tschirner-Vinke, G. (2022). AI-Based Assistance System for Manufacturing.
ETFA, Stuttgart. https://doi.org/10.1109/ETFA52439.2022.9921520
bibtex: '@article{Deppe_Brandt_Brünninghaus_Papenkordt_Heindorf_Tschirner-Vinke_2022,
series={2022 IEEE 27th International Conference on Emerging Technologies and Factory
Automation (ETFA)}, title={AI-Based Assistance System for Manufacturing}, DOI={10.1109/ETFA52439.2022.9921520},
author={Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt,
Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}, year={2022}, collection={2022
IEEE 27th International Conference on Emerging Technologies and Factory Automation
(ETFA)} }'
chicago: Deppe, Sahar, Lukas Brandt, Marc Brünninghaus, Jörg Papenkordt, Stefan
Heindorf, and Gudrun Tschirner-Vinke. “AI-Based Assistance System for Manufacturing.”
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation
(ETFA), 2022. https://doi.org/10.1109/ETFA52439.2022.9921520.
ieee: 'S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, and G.
Tschirner-Vinke, “AI-Based Assistance System for Manufacturing.” 2022, doi: 10.1109/ETFA52439.2022.9921520.'
mla: Deppe, Sahar, et al. AI-Based Assistance System for Manufacturing. 2022,
doi:10.1109/ETFA52439.2022.9921520.
short: S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, G. Tschirner-Vinke,
(2022).
conference:
end_date: 2022-09-09
location: Stuttgart
name: ETFA
start_date: 2022-09-06
date_created: 2022-10-28T11:43:49Z
date_updated: 2023-11-23T08:07:51Z
department:
- _id: '178'
- _id: '574'
- _id: '184'
doi: 10.1109/ETFA52439.2022.9921520
keyword:
- Assistance system
- Knowledge graph
- Information retrieval
- Neural networks
- AR
language:
- iso: eng
project:
- _id: '409'
grant_number: 02L19C115
name: 'KIAM: KIAM: Kompetenzzentrum KI in der Arbeitswelt des industriellen Mittelstands
in OstWestfalenLippe'
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9921520
series_title: 2022 IEEE 27th International Conference on Emerging Technologies and
Factory Automation (ETFA)
status: public
title: AI-Based Assistance System for Manufacturing
type: conference
user_id: '44648'
year: '2022'
...
---
_id: '32509'
abstract:
- lang: eng
text: " We consider fact-checking approaches that aim to predict the veracity of
assertions in knowledge graphs. Five main categories of fact-checking approaches
for knowledge graphs have been proposed in the recent literature, of\r\nwhich
each is subject to partially overlapping limitations. In particular, current text-based
approaches are limited by manual feature engineering. Path-based and rule-based
approaches are limited by their exclusive use of knowledge graphs as background
knowledge, and embedding-based approaches suffer from low accuracy scores on current
fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits
the diversity of existing categories of fact-checking approaches within an ensemble
learning setting to achieve a significantly better prediction performance. In
particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms
of Area Under the Receiver Operating Characteristic curve on the FactBench dataset.
Our code is open-source and can be found at https://github.com/dice-group/HybridFC."
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: Muhammad
full_name: Saleem, Muhammad
last_name: Saleem
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Qudus U, Röder M, Saleem M, Ngonga Ngomo A-C. HybridFC: A Hybrid Fact-Checking
Approach for Knowledge Graphs. In: Sattler U, Hogan A, Keet M, Presutti V, eds.
The Semantic Web -- ISWC 2022. Springer International Publishing; :462--480.
doi:10.1007/978-3-031-19433-7_27'
apa: 'Qudus, U., Röder, M., Saleem, M., & Ngonga Ngomo, A.-C. (n.d.). HybridFC:
A Hybrid Fact-Checking Approach for Knowledge Graphs. In U. Sattler, A. Hogan,
M. Keet, & V. Presutti (Eds.), The Semantic Web -- ISWC 2022 (pp. 462--480).
Springer International Publishing. https://doi.org/10.1007/978-3-031-19433-7_27'
bibtex: '@inproceedings{Qudus_Röder_Saleem_Ngonga Ngomo, place={Cham}, title={HybridFC:
A Hybrid Fact-Checking Approach for Knowledge Graphs}, DOI={10.1007/978-3-031-19433-7_27},
booktitle={The Semantic Web -- ISWC 2022}, publisher={Springer International Publishing},
author={Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo,
Axel-Cyrille}, editor={Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti,
Valentina}, pages={462--480} }'
chicago: 'Qudus, Umair, Michael Röder, Muhammad Saleem, and Axel-Cyrille Ngonga
Ngomo. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs.” In The
Semantic Web -- ISWC 2022, edited by Ulrike Sattler, Aidan Hogan, Maria Keet,
and Valentina Presutti, 462--480. Cham: Springer International Publishing, n.d.
https://doi.org/10.1007/978-3-031-19433-7_27.'
ieee: 'U. Qudus, M. Röder, M. Saleem, and A.-C. Ngonga Ngomo, “HybridFC: A Hybrid
Fact-Checking Approach for Knowledge Graphs,” in The Semantic Web -- ISWC 2022,
Hanghzou, China, pp. 462--480, doi: 10.1007/978-3-031-19433-7_27.'
mla: 'Qudus, Umair, et al. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge
Graphs.” The Semantic Web -- ISWC 2022, edited by Ulrike Sattler et al.,
Springer International Publishing, pp. 462--480, doi:10.1007/978-3-031-19433-7_27.'
short: 'U. Qudus, M. Röder, M. Saleem, A.-C. Ngonga Ngomo, in: U. Sattler, A. Hogan,
M. Keet, V. Presutti (Eds.), The Semantic Web -- ISWC 2022, Springer International
Publishing, Cham, n.d., pp. 462--480.'
conference:
end_date: 2022-10-27
location: Hanghzou, China
name: International Semantic Web Conference (ISWC)
start_date: 2022-10-23
date_created: 2022-08-02T11:56:03Z
date_updated: 2025-09-11T09:37:16Z
ddc:
- '000'
department:
- _id: '34'
doi: 10.1007/978-3-031-19433-7_27
editor:
- first_name: Ulrike
full_name: Sattler, Ulrike
last_name: Sattler
- first_name: Aidan
full_name: Hogan, Aidan
last_name: Hogan
- first_name: Maria
full_name: Keet, Maria
last_name: Keet
- first_name: Valentina
full_name: Presutti, Valentina
last_name: Presutti
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2022-12-22T15:45:29Z
date_updated: 2022-12-22T15:45:29Z
file_id: '34853'
file_name: hybrid_fact_check_iswc2022.pdf
file_size: 296218
relation: main_file
success: 1
file_date_updated: 2022-12-22T15:45:29Z
has_accepted_license: '1'
keyword:
- fact checking · ensemble learning · knowledge graph veracit
language:
- iso: eng
page: 462--480
place: Cham
popular_science: '1'
project:
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: The Semantic Web -- ISWC 2022
publication_identifier:
isbn:
- 978-3-031-19433-7
publication_status: accepted
publisher: Springer International Publishing
quality_controlled: '1'
status: public
title: 'HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs'
type: conference
user_id: '83392'
year: '2022'
...
---
_id: '27381'
abstract:
- lang: eng
text: Graph neural networks (GNNs) have been successfully applied in many structured
data domains, with applications ranging from molecular property prediction to
the analysis of social networks. Motivated by the broad applicability of GNNs,
we propose the family of so-called RankGNNs, a combination of neural Learning
to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
between graphs, suggesting that one of them is preferred over the other. One practical
application of this problem is drug screening, where an expert wants to find the
most promising molecules in a large collection of drug candidates. We empirically
demonstrate that our proposed pair-wise RankGNN approach either significantly
outperforms or at least matches the ranking performance of the naive point-wise
baseline approach, in which the LtR problem is solved via GNN-based graph regression.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference
on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science.
Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5'
apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph
Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th
International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180).
Springer. https://doi.org/10.1007/978-3-030-88942-5
bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
DOI={10.1007/978-3-030-88942-5},
booktitle={Proceedings of The 24th International Conference on Discovery Science
(DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
Notes in Computer Science} }'
chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
Graph Neural Networks.” In Proceedings of The 24th International Conference
on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80.
Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.
ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
Networks,” in Proceedings of The 24th International Conference on Discovery
Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.'
mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
Neural Networks.” Proceedings of The 24th International Conference on Discovery
Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.
short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
pp. 166–180.'
conference:
end_date: 2021-10-13
location: Halifax, Canada
name: 24th International Conference on Discovery Science
start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
full_name: Soares, Carlos
last_name: Soares
- first_name: Luis
full_name: Torgo, Luis
last_name: Torgo
external_id:
arxiv:
- '2104.08869'
intvolume: ' 12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
(DS 2021)
publication_identifier:
isbn:
- '9783030889418'
- '9783030889425'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '48881'
abstract:
- lang: eng
text: 'Classic automated algorithm selection (AS) for (combinatorial) optimization
problems heavily relies on so-called instance features, i.e., numerical characteristics
of the problem at hand ideally extracted with computationally low-demanding routines.
For the traveling salesperson problem (TSP) a plethora of features have been suggested.
Most of these features are, if at all, only normalized imprecisely raising the
issue of feature values being strongly affected by the instance size. Such artifacts
may have detrimental effects on algorithm selection models. We propose a normalization
for two feature groups which stood out in multiple AS studies on the TSP: (a)
features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph
(NNG) transformation of the input instance. To this end we theoretically derive
minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs.
We analyze the differences in feature space between normalized versions of these
features and their unnormalized counterparts. Our empirical investigations on
various TSP benchmark sets point out that the feature scaling succeeds in eliminating
the effect of the instance size. Eventually, a proof-of-concept AS-study shows
promising results: models trained with normalized features tend to outperform
those trained with the respective vanilla features.'
author:
- first_name: Jonathan
full_name: Heins, Jonathan
last_name: Heins
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Moritz
full_name: Seiler, Moritz
last_name: Seiler
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
citation:
ama: 'Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential
of Normalized TSP Features for Automated Algorithm Selection. In: Proceedings
of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association
for Computing Machinery; 2021:1–15.'
apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke,
P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm
Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
bibtex: '@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York,
NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm
Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins,
Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann,
Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }'
chicago: 'Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann,
and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” In Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, 1–15. New York, NY, USA: Association for Computing
Machinery, 2021.'
ieee: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On
the Potential of Normalized TSP Features for Automated Algorithm Selection,” in
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.'
mla: Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated
Algorithm Selection.” Proceedings of the 16th ACM/SIGEVO Conference on Foundations
of Genetic Algorithms, Association for Computing Machinery, 2021, pp. 1–15.
short: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in:
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms,
Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.'
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:23Z
department:
- _id: '819'
extern: '1'
keyword:
- automated algorithm selection
- graph theory
- instance features
- normalization
- traveling salesperson problem (TSP)
language:
- iso: eng
page: 1–15
place: New York, NY, USA
publication: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic
Algorithms
publication_identifier:
isbn:
- 978-1-4503-8352-3
publisher: Association for Computing Machinery
status: public
title: On the Potential of Normalized TSP Features for Automated Algorithm Selection
type: book_chapter
user_id: '102979'
year: '2021'
...
---
_id: '19953'
abstract:
- lang: eng
text: Current GNN architectures use a vertex neighborhood aggregation scheme, which
limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman
(WL) graph isomorphism test. Here, we propose a novel graph convolution operator
that is based on the 2-dimensional WL test. We formally show that the resulting
2-WL-GNN architecture is more discriminative than existing GNN approaches. This
theoretical result is complemented by experimental studies using synthetic and
real data. On multiple common graph classification benchmarks, we demonstrate
that the proposed model is competitive with state-of-the-art graph kernels and
GNNs.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman
Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th
Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of
Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.'
apa: 'Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order
Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
(Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.'
bibtex: '@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand},
series={Proceedings of Machine Learning Research}, title={A Novel Higher-order
Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of
the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR},
author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin
Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings
of Machine Learning Research} }'
chicago: 'Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order
Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference
on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama,
129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR,
2020.'
ieee: C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman
Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine
Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.
mla: Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.”
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.
short: 'C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR,
Bangkok, Thailand, 2020, pp. 49–64.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-10-08T10:48:38Z
date_updated: 2022-01-06T06:54:17Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Sinno
full_name: Jialin Pan, Sinno
last_name: Jialin Pan
- first_name: Masashi
full_name: Sugiyama, Masashi
last_name: Sugiyama
external_id:
arxiv:
- '2007.00346'
file:
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:48Z
date_updated: 2020-10-08T11:21:00Z
file_id: '19954'
file_name: damke20.pdf
file_size: 771137
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:59Z
date_updated: 2020-10-08T11:24:29Z
file_id: '19955'
file_name: damke20-supp.pdf
file_size: 613163
relation: supplementary_material
file_date_updated: 2020-10-08T11:24:29Z
has_accepted_license: '1'
intvolume: ' 129'
keyword:
- graph neural networks
- Weisfeiler-Lehman test
- cycle detection
language:
- iso: eng
oa: '1'
page: 49-64
place: Bangkok, Thailand
publication: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
publication_status: published
publisher: PMLR
quality_controlled: '1'
series_title: Proceedings of Machine Learning Research
status: public
title: A Novel Higher-order Weisfeiler-Lehman Graph Convolution
type: conference
user_id: '48192'
volume: 129
year: '2020'
...
---
_id: '10129'
abstract:
- lang: eng
text: There are many hard conjectures in graph theory, like Tutte's 5-flow conjecture,
and the 5-cycle double cover conjecture, which would be true in general if they
would be true for cubic graphs. Since most of them are trivially true for 3-edge-colorable
cubic graphs, cubic graphs which are not 3-edge-colorable, often called snarks,
play a key role in this context. Here, we survey parameters measuring how far
apart a non 3-edge-colorable graph is from being 3-edge-colorable. We study their
interrelation and prove some new results. Besides getting new insight into the
structure of snarks, we show that such measures give partial results with respect
to these important conjectures. The paper closes with a list of open problems
and conjectures.
article_number: P4.54
article_type: original
author:
- first_name: M. A.
full_name: Fiol, M. A.
last_name: Fiol
- first_name: Guiseppe
full_name: Mazzuoccolo, Guiseppe
last_name: Mazzuoccolo
- first_name: Eckhard
full_name: Steffen, Eckhard
id: '15548'
last_name: Steffen
citation:
ama: Fiol MA, Mazzuoccolo G, Steffen E. Measures of Edge-Uncolorability of Cubic
Graphs. The Electronic Journal of Combinatorics. 2018;25(4).
apa: Fiol, M. A., Mazzuoccolo, G., & Steffen, E. (2018). Measures of Edge-Uncolorability
of Cubic Graphs. The Electronic Journal of Combinatorics, 25(4).
bibtex: '@article{Fiol_Mazzuoccolo_Steffen_2018, title={Measures of Edge-Uncolorability
of Cubic Graphs}, volume={25}, number={4P4.54}, journal={The Electronic Journal
of Combinatorics}, author={Fiol, M. A. and Mazzuoccolo, Guiseppe and Steffen,
Eckhard}, year={2018} }'
chicago: Fiol, M. A., Guiseppe Mazzuoccolo, and Eckhard Steffen. “Measures of Edge-Uncolorability
of Cubic Graphs.” The Electronic Journal of Combinatorics 25, no. 4 (2018).
ieee: M. A. Fiol, G. Mazzuoccolo, and E. Steffen, “Measures of Edge-Uncolorability
of Cubic Graphs,” The Electronic Journal of Combinatorics, vol. 25, no.
4, 2018.
mla: Fiol, M. A., et al. “Measures of Edge-Uncolorability of Cubic Graphs.” The
Electronic Journal of Combinatorics, vol. 25, no. 4, P4.54, 2018.
short: M.A. Fiol, G. Mazzuoccolo, E. Steffen, The Electronic Journal of Combinatorics
25 (2018).
date_created: 2019-06-05T09:59:10Z
date_updated: 2022-01-06T06:50:30Z
department:
- _id: '542'
intvolume: ' 25'
issue: '4'
keyword:
- Cubic graph
- Tait coloring
- Snark
- Boole coloring
- Berge's conjecture
- Tutte's 5-flow conjecture
language:
- iso: eng
publication: The Electronic Journal of Combinatorics
status: public
title: Measures of Edge-Uncolorability of Cubic Graphs
type: journal_article
user_id: '15540'
volume: 25
year: '2018'
...
---
_id: '61025'
abstract:
- lang: eng
text: The concept of social dominance has been used in a plethora of studies to
assess animal behaviour and relationships between individuals for nearly a century.
Nevertheless, a standard approach does not yet exist to assess dominance in species
that have a nonlinear or weakly linear hierarchical structure. We amassed 316
published data sets and show that 73.7% of the data sets and 90.3% of 103 species
that we reviewed do not have a strongly linear structure. Herein, we present a
novel method, ADAGIO, for assessing the structure of dominance networks. ADAGIO
computes dominance hierarchies, in the form of directed acyclic graphs, to represent
the dominance relations of a given group of animals. Thus far, most methods for
computing dominance ranks assume implicitly that the dominance relation is a total
order of the individuals in a group. ADAGIO does not assume or require this to
be always true, and is hence more appropriate for analysing dominance hierarchies
that are not strongly linear. We evaluated our approach against other frequently
used methods, I&SI, David's score and Elo-rating, on 12 000 simulated data sets
and on 279 interaction matrices from published, empirical data. The results from
the simulated data show that ADAGIO achieves a significantly smaller error, and
hence performs better when assigning ranks than other methods. Additionally, ADAGIO
generated accurate dominance hierarchies for empirical data sets with a high index
of linearity. Hence, our findings suggest that ADAGIO is currently the most reliable
method to assess social dominance in gregarious animals living in groups of any
size. Furthermore, since ADAGIO was designed to be generic, its applicability
has the potential to extend beyond dominance data. The source code of our algorithm
and all simulations used for this paper are publicly available at http://ngonga.github.io/adagio/.
article_type: original
author:
- first_name: Pamela Heidi
full_name: Douglas, Pamela Heidi
id: '72311'
last_name: Douglas
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Gottfried
full_name: Hohmann, Gottfried
last_name: Hohmann
citation:
ama: Douglas PH, Ngonga Ngomo A-C, Hohmann G. A novel approach for dominance assessment
in gregarious species: ADAGIO. Animal Behaviour. 2016;123:21-32. doi:10.1016/j.anbehav.2016.10.014
apa: Douglas, P. H., Ngonga Ngomo, A.-C., & Hohmann, G. (2016). A novel approach
for dominance assessment in gregarious species: ADAGIO. Animal Behaviour,
123, 21–32. https://doi.org/10.1016/j.anbehav.2016.10.014
bibtex: '@article{Douglas_Ngonga Ngomo_Hohmann_2016, title={A novel approach for
dominance assessment in gregarious species: ADAGIO}, volume={123}, DOI={10.1016/j.anbehav.2016.10.014},
journal={Animal Behaviour}, publisher={Elsevier BV}, author={Douglas, Pamela Heidi
and Ngonga Ngomo, Axel-Cyrille and Hohmann, Gottfried}, year={2016}, pages={21–32}
}'
chicago: 'Douglas, Pamela Heidi, Axel-Cyrille Ngonga Ngomo, and Gottfried Hohmann.
“A Novel Approach for Dominance Assessment in Gregarious Species: ADAGIO.” Animal
Behaviour 123 (2016): 21–32. https://doi.org/10.1016/j.anbehav.2016.10.014.'
ieee: 'P. H. Douglas, A.-C. Ngonga Ngomo, and G. Hohmann, “A novel approach for
dominance assessment in gregarious species: ADAGIO,” Animal Behaviour,
vol. 123, pp. 21–32, 2016, doi: 10.1016/j.anbehav.2016.10.014.'
mla: Douglas, Pamela Heidi, et al. “A Novel Approach for Dominance Assessment in
Gregarious Species: ADAGIO.” Animal Behaviour, vol. 123, Elsevier BV, 2016,
pp. 21–32, doi:10.1016/j.anbehav.2016.10.014.
short: P.H. Douglas, A.-C. Ngonga Ngomo, G. Hohmann, Animal Behaviour 123 (2016)
21–32.
date_created: 2025-08-26T19:24:18Z
date_updated: 2025-08-26T19:57:38Z
department:
- _id: '40'
doi: 10.1016/j.anbehav.2016.10.014
extern: '1'
intvolume: ' 123'
keyword:
- aggression
- behaviour
- comparability
- directed acyclic graph
- hierarchy
- linearity
- nonlinearity
- social rank
- totality
language:
- iso: eng
page: 21-32
publication: Animal Behaviour
publication_identifier:
issn:
- 0003-3472
publication_status: published
publisher: Elsevier BV
status: public
title: A novel approach for dominance assessment in gregarious species: ADAGIO
type: journal_article
user_id: '72311'
volume: 123
year: '2016'
...