---
_id: '61123'
abstract:
- lang: eng
text: Knowledge graphs are used by a growing number of applications to represent
structured data. Hence, evaluating the veracity of assertions in knowledge graphs—dubbed
fact checking—is currently a challenge of growing importance. However, manual
fact checking is commonly impractical due to the sheer size of knowledge graphs.
This paper is a systematic survey of recent works on automatic fact checking with
a focus on knowledge graphs. We present recent fact-checking approaches, the varied
sources they use as background knowledge, and the features they rely upon. Finally,
we draw conclusions pertaining to possible future research directions in fact
checking knowledge graphs.
article_number: '3749838'
article_type: original
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. Fact Checking Knowledge Graphs
-- A Survey. ACM Computing Surveys. 2025;58. doi:10.1145/3749838
apa: Qudus, U., Röder, M., Saleem, M., & Ngonga Ngomo, A.-C. (2025). Fact Checking
Knowledge Graphs -- A Survey. ACM Computing Surveys, 58, Article
3749838. https://doi.org/10.1145/3749838
bibtex: '@article{Qudus_Röder_Saleem_Ngonga Ngomo_2025, title={Fact Checking Knowledge
Graphs -- A Survey}, volume={58}, DOI={10.1145/3749838},
number={3749838}, journal={ACM Computing Surveys}, publisher={Association for
Computing Machinery (ACM)}, author={Qudus, Umair and Röder, Michael and Saleem,
Muhammad and Ngonga Ngomo, Axel-Cyrille}, year={2025} }'
chicago: Qudus, Umair, Michael Röder, Muhammad Saleem, and Axel-Cyrille Ngonga Ngomo.
“Fact Checking Knowledge Graphs -- A Survey.” ACM Computing Surveys 58
(2025). https://doi.org/10.1145/3749838.
ieee: 'U. Qudus, M. Röder, M. Saleem, and A.-C. Ngonga Ngomo, “Fact Checking Knowledge
Graphs -- A Survey,” ACM Computing Surveys, vol. 58, Art. no. 3749838,
2025, doi: 10.1145/3749838.'
mla: Qudus, Umair, et al. “Fact Checking Knowledge Graphs -- A Survey.” ACM Computing
Surveys, vol. 58, 3749838, Association for Computing Machinery (ACM), 2025,
doi:10.1145/3749838.
short: U. Qudus, M. Röder, M. Saleem, A.-C. Ngonga Ngomo, ACM Computing Surveys
58 (2025).
date_created: 2025-09-03T15:46:43Z
date_updated: 2025-09-11T09:30:28Z
ddc:
- '006'
department:
- _id: '574'
doi: 10.1145/3749838
external_id:
unknown:
- 10.1145/3749838
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2025-09-11T09:26:29Z
date_updated: 2025-09-11T09:26:29Z
file_id: '61195'
file_name: 3749838.pdf
file_size: 1062387
relation: main_file
success: 1
file_date_updated: 2025-09-11T09:26:29Z
has_accepted_license: '1'
intvolume: ' 58'
keyword:
- fact checking
- knowledge graphs
- fact-checkers
- check worthiness
- evidence retrieval
- trust
- veracity.
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/pdf/10.1145/3749838
oa: '1'
popular_science: '1'
publication: ACM Computing Surveys
publication_identifier:
issn:
- 0360-0300
- 1557-7341
publication_status: published
publisher: Association for Computing Machinery (ACM)
quality_controlled: '1'
status: public
title: Fact Checking Knowledge Graphs -- A Survey
type: journal_article
user_id: '83392'
volume: 58
year: '2025'
...
---
_id: '56983'
abstract:
- lang: eng
text: Detecting the veracity of a statement automatically is a challenge the world
is grappling with due to the vast amount of data spread across the web. Verifying
a given claim typically entails validating it within the framework of supporting
evidence like a retrieved piece of text. Classifying the stance of the text with
respect to the claim is called stance classification. Despite advancements in
automated fact-checking, most systems still rely on a substantial quantity of
labeled training data, which can be costly. In this work, we avoid the costly
training or fine-tuning of models by reusing pre-trained large language models
together with few-shot in-context learning. Since we do not train any model, our
approach ExPrompt is lightweight, demands fewer resources than other stance classification
methods and can serve as a modern baseline for future developments. At the same
time, our evaluation shows that our approach is able to outperform former state-of-the-art
stance classification approaches regarding accuracy by at least 2 percent. Our
scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt.
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: Daniel
full_name: Vollmers, Daniel
last_name: Vollmers
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Qudus U, Röder M, Vollmers D, Ngonga Ngomo A-C. ExPrompt: Augmenting Prompts
Using Examples as Modern Baseline for Stance Classification. In: Proceedings
of the 33rd ACM International Conference on Information and Knowledge Management.
Vol 9. ACM; 2024:3994-3999. doi:10.1145/3627673.3679923'
apa: 'Qudus, U., Röder, M., Vollmers, D., & Ngonga Ngomo, A.-C. (2024). ExPrompt:
Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.
Proceedings of the 33rd ACM International Conference on Information and Knowledge
Management, 9, 3994–3999. https://doi.org/10.1145/3627673.3679923'
bibtex: '@inproceedings{Qudus_Röder_Vollmers_Ngonga Ngomo_2024, title={ExPrompt:
Augmenting Prompts Using Examples as Modern Baseline for Stance Classification},
volume={9}, DOI={10.1145/3627673.3679923},
booktitle={Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management}, publisher={ACM}, author={Qudus, Umair and Röder, Michael
and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}, year={2024}, pages={3994–3999}
}'
chicago: 'Qudus, Umair, Michael Röder, Daniel Vollmers, and Axel-Cyrille Ngonga
Ngomo. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance
Classification.” In Proceedings of the 33rd ACM International Conference on
Information and Knowledge Management, 9:3994–99. ACM, 2024. https://doi.org/10.1145/3627673.3679923.'
ieee: 'U. Qudus, M. Röder, D. Vollmers, and A.-C. Ngonga Ngomo, “ExPrompt: Augmenting
Prompts Using Examples as Modern Baseline for Stance Classification,” in Proceedings
of the 33rd ACM International Conference on Information and Knowledge Management,
Boise, ID, USA, 2024, vol. 9, pp. 3994–3999, doi: 10.1145/3627673.3679923.'
mla: 'Qudus, Umair, et al. “ExPrompt: Augmenting Prompts Using Examples as Modern
Baseline for Stance Classification.” Proceedings of the 33rd ACM International
Conference on Information and Knowledge Management, vol. 9, ACM, 2024, pp.
3994–99, doi:10.1145/3627673.3679923.'
short: 'U. Qudus, M. Röder, D. Vollmers, A.-C. Ngonga Ngomo, in: Proceedings of
the 33rd ACM International Conference on Information and Knowledge Management,
ACM, 2024, pp. 3994–3999.'
conference:
end_date: 2024-10-25
location: Boise, ID, USA
name: 'CIKM ''24: Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management'
start_date: 2024-10-21
date_created: 2024-11-11T13:15:25Z
date_updated: 2025-09-11T09:49:07Z
ddc:
- '006'
doi: 10.1145/3627673.3679923
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-11-11T13:24:19Z
date_updated: 2024-11-11T13:24:19Z
file_id: '56984'
file_name: public.pdf
file_size: 531579
relation: main_file
success: 1
file_date_updated: 2024-11-11T13:24:19Z
has_accepted_license: '1'
intvolume: ' 9'
keyword:
- Stance Classification
- Few-shot in-context learning
- Pre-trained large language models
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/10.1145/3627673.3679923
page: 3994 - 3999
popular_science: '1'
project:
- _id: '412'
name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen'
publication: Proceedings of the 33rd ACM International Conference on Information and
Knowledge Management
publication_identifier:
isbn:
- 79-8-4007-0436-9/24/10
publication_status: published
publisher: ACM
quality_controlled: '1'
status: public
title: 'ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance
Classification'
type: conference
user_id: '83392'
volume: 9
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: '50796'
abstract:
- lang: eng
text: "Verifying assertions is an essential part of creating and maintaining knowledge
graphs. Most often, this task cannot be carried out manually due to the sheer
size of modern knowledge graphs. Hence, automatic fact-checking approaches have
been proposed over the last decade. These approaches aim to compute automatically
whether a given assertion is correct or incorrect. However, most fact-checking
approaches are binary classifiers that fail to consider the volatility of some
assertions, i.e., the fact that such assertions are only valid at certain times
or for specific time intervals. Moreover, the few approaches able to predict when
an assertion was valid (i.e., time-point prediction approaches) rely on manual
feature engineering. This paper presents T EMPORAL FC, a temporal fact-checking
approach that uses multiple sources of background knowledge to assess the veracity
and temporal validity of a given assertion. We evaluate T EMPORAL FC\r\non two
datasets and compare it to the state of the art in fact-checking and time-point
prediction. Our results suggest that T EMPORAL FC outperforms the state of the
art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the\r\nReceiver
Operating Characteristic curve and on the time-point prediction task by 0.25 to
0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found
at https://github.com/dice-group/TemporalFC."
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: Sabrina
full_name: Kirrane, Sabrina
last_name: Kirrane
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Qudus U, Röder M, Kirrane S, Ngonga Ngomo A-C. TemporalFC: A Temporal Fact
Checking approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et
al., eds. The Semantic Web – ISWC 2023. Vol 14265. Lecture Notes in Computer
Science. Springer International Publishing; 2023:465–483. doi:10.1007/978-3-031-47240-4_25'
apa: 'Qudus, U., Röder, M., Kirrane, S., & Ngonga Ngomo, A.-C. (2023). TemporalFC:
A Temporal Fact Checking approach over Knowledge Graphs. In T. R. Payne, V. Presutti,
G. Qi, M. Poveda-Villalónt, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &
J. Li (Eds.), The Semantic Web – ISWC 2023 (Vol. 14265, pp. 465–483). Springer
International Publishing. https://doi.org/10.1007/978-3-031-47240-4_25'
bibtex: '@inproceedings{Qudus_Röder_Kirrane_Ngonga Ngomo_2023, place={Cham}, series={Lecture
Notes in Computer Science}, title={TemporalFC: A Temporal Fact Checking approach
over Knowledge Graphs}, volume={14265}, DOI={10.1007/978-3-031-47240-4_25},
booktitle={The Semantic Web – ISWC 2023}, publisher={Springer International Publishing},
author={Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngonga Ngomo,
Axel-Cyrille}, editor={R. Payne, Terry and Presutti, Valentina and Qi, Guilin
and Poveda-Villalónt, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi,
Zoi and Cheng, Gong and Li, Juanzi}, year={2023}, pages={465–483}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Qudus, Umair, Michael Röder, Sabrina Kirrane, and Axel-Cyrille Ngonga
Ngomo. “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs.”
In The Semantic Web – ISWC 2023, edited by Terry R. Payne, Valentina Presutti,
Guilin Qi, María Poveda-Villalónt, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi,
Gong Cheng, and Juanzi Li, 14265:465–483. Lecture Notes in Computer Science. Cham:
Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-47240-4_25.'
ieee: 'U. Qudus, M. Röder, S. Kirrane, and A.-C. Ngonga Ngomo, “TemporalFC: A Temporal
Fact Checking approach over Knowledge Graphs,” in The Semantic Web – ISWC 2023,
2023, vol. 14265, pp. 465–483, doi: 10.1007/978-3-031-47240-4_25.'
mla: 'Qudus, Umair, et al. “TemporalFC: A Temporal Fact Checking Approach over Knowledge
Graphs.” The Semantic Web – ISWC 2023, edited by Terry R. Payne et al.,
vol. 14265, Springer International Publishing, 2023, pp. 465–483, doi:10.1007/978-3-031-47240-4_25.'
short: 'U. Qudus, M. Röder, S. Kirrane, A.-C. Ngonga Ngomo, in: T. R. Payne, V.
Presutti, G. Qi, M. Poveda-Villalónt, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng,
J. Li (Eds.), The Semantic Web – ISWC 2023, Springer International Publishing,
Cham, 2023, pp. 465–483.'
date_created: 2024-01-23T11:38:26Z
date_updated: 2025-09-11T09:34:39Z
ddc:
- '006'
doi: 10.1007/978-3-031-47240-4_25
editor:
- first_name: Terry
full_name: R. Payne, Terry
last_name: R. Payne
- first_name: Valentina
full_name: Presutti, Valentina
last_name: Presutti
- first_name: Guilin
full_name: Qi, Guilin
last_name: Qi
- first_name: María
full_name: Poveda-Villalónt, María
last_name: Poveda-Villalónt
- first_name: Giorgos
full_name: Stoilos, Giorgos
last_name: Stoilos
- first_name: Laura
full_name: Hollink, Laura
last_name: Hollink
- first_name: Zoi
full_name: Kaoudi, Zoi
last_name: Kaoudi
- first_name: Gong
full_name: Cheng, Gong
last_name: Cheng
- first_name: Juanzi
full_name: Li, Juanzi
last_name: Li
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2025-09-11T09:33:14Z
date_updated: 2025-09-11T09:33:14Z
file_id: '61196'
file_name: temporalfcc.pdf
file_size: 1938151
relation: main_file
success: 1
file_date_updated: 2025-09-11T09:33:14Z
has_accepted_license: '1'
intvolume: ' 14265'
keyword:
- knowgraphs enexa sail nebulaproject dice ngonga saleem roeder qudus
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2023/ISWC_TemporalFC/public.pdf
oa: '1'
page: 465–483
place: Cham
popular_science: '1'
publication: The Semantic Web – ISWC 2023
publisher: Springer International Publishing
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: 'TemporalFC: A Temporal Fact Checking approach over Knowledge Graphs'
type: conference
user_id: '83392'
volume: 14265
year: '2023'
...
---
_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: '25212'
abstract:
- lang: eng
text: "Finding a good query plan is key to the optimization of query runtime. This
holds in particular for cost-based federation\r\nengines, which make use of cardinality
estimations to achieve this goal. A number of studies compare SPARQL federation
engines across different performance metrics, including query runtime, result
set completeness and correctness, number of sources selected and number of requests
sent. Albeit informative, these metrics are generic and unable to quantify and
evaluate the accuracy of the cardinality estimators of cost-based federation engines.
To thoroughly evaluate cost-based federation engines, the effect of estimated
cardinality errors on the overall query runtime performance must be measured.
In this paper, we address this challenge by presenting novel evaluation metrics
targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines.
We evaluate five cost-based federated SPARQL query engines using existing as well
as novel evaluation metrics by using LargeRDFBench queries. Our results provide
a detailed analysis of the experimental outcomes that reveal novel insights, useful
for the development of future cost-based federated SPARQL query processing engines."
article_type: original
author:
- first_name: Umair
full_name: Qudus, Umair
id: '83392'
last_name: Qudus
orcid: 0000-0001-6714-8729
- 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
- first_name: Young-Koo
full_name: Lee, Young-Koo
last_name: Lee
citation:
ama: Qudus U, Saleem M, Ngonga Ngomo A-C, Lee Y-K. An Empirical Evaluation of Cost-based
Federated SPARQL Query Processing Engines. Semantic Web. 12(6):843-868.
doi:10.3233/SW-200420
apa: Qudus, U., Saleem, M., Ngonga Ngomo, A.-C., & Lee, Y.-K. (n.d.). An Empirical
Evaluation of Cost-based Federated SPARQL Query Processing Engines. Semantic
Web, 12(6), 843–868. https://doi.org/10.3233/SW-200420
bibtex: '@article{Qudus_Saleem_Ngonga Ngomo_Lee, title={An Empirical Evaluation
of Cost-based Federated SPARQL Query Processing Engines}, volume={12}, DOI={10.3233/SW-200420}, number={6}, journal={Semantic
Web}, publisher={ISO Press}, author={Qudus, Umair and Saleem, Muhammad and Ngonga
Ngomo, Axel-Cyrille and Lee, Young-Koo}, pages={843–868} }'
chicago: 'Qudus, Umair, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo, and Young-Koo
Lee. “An Empirical Evaluation of Cost-Based Federated SPARQL Query Processing
Engines.” Semantic Web 12, no. 6 (n.d.): 843–68. https://doi.org/10.3233/SW-200420.'
ieee: 'U. Qudus, M. Saleem, A.-C. Ngonga Ngomo, and Y.-K. Lee, “An Empirical Evaluation
of Cost-based Federated SPARQL Query Processing Engines,” Semantic Web,
vol. 12, no. 6, pp. 843–868, doi: 10.3233/SW-200420.'
mla: Qudus, Umair, et al. “An Empirical Evaluation of Cost-Based Federated SPARQL
Query Processing Engines.” Semantic Web, vol. 12, no. 6, ISO Press, pp.
843–68, doi:10.3233/SW-200420.
short: U. Qudus, M. Saleem, A.-C. Ngonga Ngomo, Y.-K. Lee, Semantic Web 12 (n.d.)
843–868.
date_created: 2021-10-01T06:52:52Z
date_updated: 2025-09-11T09:50:14Z
ddc:
- '000'
department:
- _id: '574'
doi: 10.3233/SW-200420
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-01-13T11:35:53Z
date_updated: 2024-01-13T11:35:53Z
file_id: '50483'
file_name: swj2604.pdf
file_size: 978478
relation: main_file
success: 1
file_date_updated: 2024-01-13T11:35:53Z
has_accepted_license: '1'
intvolume: ' 12'
issue: '6'
keyword:
- SPARQL
- benchmarking
- cost-based
- cost-free
- federated
- querying
language:
- iso: eng
page: 843-868
project:
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: Semantic Web
publication_identifier:
issn:
- 2210-4968
publication_status: accepted
publisher: ISO Press
status: public
title: An Empirical Evaluation of Cost-based Federated SPARQL Query Processing Engines
type: journal_article
user_id: '83392'
volume: 12
year: '2021'
...