---
_id: '61123'
abstract:
- lang: eng
  text: <jats:p>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.</jats:p>
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. <i>ACM Computing Surveys</i>. 2025;58. doi:<a href="https://doi.org/10.1145/3749838">10.1145/3749838</a>
  apa: Qudus, U., Röder, M., Saleem, M., &#38; Ngonga Ngomo, A.-C. (2025). Fact Checking
    Knowledge Graphs -- A Survey. <i>ACM Computing Surveys</i>, <i>58</i>, Article
    3749838. <a href="https://doi.org/10.1145/3749838">https://doi.org/10.1145/3749838</a>
  bibtex: '@article{Qudus_Röder_Saleem_Ngonga Ngomo_2025, title={Fact Checking Knowledge
    Graphs -- A Survey}, volume={58}, DOI={<a href="https://doi.org/10.1145/3749838">10.1145/3749838</a>},
    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.” <i>ACM Computing Surveys</i> 58
    (2025). <a href="https://doi.org/10.1145/3749838">https://doi.org/10.1145/3749838</a>.
  ieee: 'U. Qudus, M. Röder, M. Saleem, and A.-C. Ngonga Ngomo, “Fact Checking Knowledge
    Graphs -- A Survey,” <i>ACM Computing Surveys</i>, vol. 58, Art. no. 3749838,
    2025, doi: <a href="https://doi.org/10.1145/3749838">10.1145/3749838</a>.'
  mla: Qudus, Umair, et al. “Fact Checking Knowledge Graphs -- A Survey.” <i>ACM Computing
    Surveys</i>, vol. 58, 3749838, Association for Computing Machinery (ACM), 2025,
    doi:<a href="https://doi.org/10.1145/3749838">10.1145/3749838</a>.
  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: '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: <i>Proceedings of the Thirteenth International Conference on Knowledge
    Capture(K-CAP 2025)</i>. ACM; 2025. doi:<a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>'
  apa: Sapkota, R., Demir, C., Sharma, A., &#38; Ngonga Ngomo, A.-C. (2025). Parameter
    Averaging in Link Prediction. <i>Proceedings of the Thirteenth International Conference
    on Knowledge Capture(K-CAP 2025)</i>. Knowledge Capture Conference 2025, Dayton,
    OH, USA. <a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>
  bibtex: '@inproceedings{Sapkota_Demir_Sharma_Ngonga Ngomo_2025, place={Dayton, OH,
    USA}, title={Parameter Averaging in Link Prediction}, DOI={<a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>},
    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 <i>Proceedings of the Thirteenth
    International Conference on Knowledge Capture(K-CAP 2025)</i>. Dayton, OH, USA:
    ACM, 2025. <a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>.'
  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: <a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>.'
  mla: Sapkota, Rupesh, et al. “Parameter Averaging in Link Prediction.” <i>Proceedings
    of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)</i>,
    ACM, 2025, doi:<a href="https://doi.org/10.1145/3731443.3771365">https://doi.org/10.1145/3731443.3771365</a>.
  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: '50479'
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 TEMPORALFC, 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 TEMPORALFC on two datasets
    and compare it to the state of the art in fact-checking and time-point prediction.
    Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking
    task by 0.13 to 0.15 in terms of Area Under the Receiver 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
  last_name: Qudus
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Sabrina
  full_name: Kirrane, Sabrina
  last_name: Kirrane
- first_name: Axel-Cyrille Ngonga
  full_name: Ngomo, Axel-Cyrille Ngonga
  last_name: Ngomo
citation:
  ama: 'Qudus U, Röder M, Kirrane S, Ngomo A-CN. TemporalFC: A Temporal Fact Checking
    Approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et al., eds.
    <i>The Semantic Web – ISWC 2023</i>. Vol 14265.  Lecture Notes in Computer Science.
    Springer, Cham; 2023:465–483. doi:<a href="https://doi.org/10.1007/978-3-031-47240-4_25">10.1007/978-3-031-47240-4_25</a>'
  apa: 'Qudus, U., Röder, M., Kirrane, S., &#38; Ngomo, A.-C. N. (2023). TemporalFC:
    A Temporal Fact Checking Approach over Knowledge Graphs. In T. R. Payne, V. Presutti,
    G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &#38;
    J. Li (Eds.), <i>The Semantic Web – ISWC 2023</i> (Vol. 14265, pp. 465–483). Springer,
    Cham. <a href="https://doi.org/10.1007/978-3-031-47240-4_25">https://doi.org/10.1007/978-3-031-47240-4_25</a>'
  bibtex: '@inproceedings{Qudus_Röder_Kirrane_Ngomo_2023, place={Cham}, series={ Lecture
    Notes in Computer Science}, title={TemporalFC: A Temporal Fact Checking Approach
    over Knowledge Graphs}, volume={14265}, DOI={<a href="https://doi.org/10.1007/978-3-031-47240-4_25">10.1007/978-3-031-47240-4_25</a>},
    booktitle={The Semantic Web – ISWC 2023}, publisher={Springer, Cham}, author={Qudus,
    Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga},
    editor={R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón,
    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 <i>The Semantic Web – ISWC 2023</i>, edited by Terry R. Payne, Valentina Presutti,
    Guilin Qi, María Poveda-Villalón, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi,
    Gong Cheng, and Juanzi Li, 14265:465–483.  Lecture Notes in Computer Science.
    Cham: Springer, Cham, 2023. <a href="https://doi.org/10.1007/978-3-031-47240-4_25">https://doi.org/10.1007/978-3-031-47240-4_25</a>.'
  ieee: 'U. Qudus, M. Röder, S. Kirrane, and A.-C. N. Ngomo, “TemporalFC: A Temporal
    Fact Checking Approach over Knowledge Graphs,” in <i>The Semantic Web – ISWC 2023</i>,
    Athens, Greece, 2023, vol. 14265, pp. 465–483, doi: <a href="https://doi.org/10.1007/978-3-031-47240-4_25">10.1007/978-3-031-47240-4_25</a>.'
  mla: 'Qudus, Umair, et al. “TemporalFC: A Temporal Fact Checking Approach over Knowledge
    Graphs.” <i>The Semantic Web – ISWC 2023</i>, edited by Terry R. Payne et al.,
    vol. 14265, Springer, Cham, 2023, pp. 465–483, doi:<a href="https://doi.org/10.1007/978-3-031-47240-4_25">10.1007/978-3-031-47240-4_25</a>.'
  short: 'U. Qudus, M. Röder, S. Kirrane, A.-C.N. Ngomo, in: T. R. Payne, V. Presutti,
    G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, J. Li
    (Eds.), The Semantic Web – ISWC 2023, Springer, Cham, Cham, 2023, pp. 465–483.'
conference:
  end_date: 2023-11-10
  location: Athens, Greece
  name: The Semantic Web – ISWC 2023
  start_date: 2023-11-06
date_created: 2024-01-13T11:22:15Z
date_updated: 2024-01-13T11:48:28Z
ddc:
- '006'
department:
- _id: '34'
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-Villalón, María
  last_name: Poveda-Villalón
- 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: 2024-01-13T11:25:48Z
  date_updated: 2024-01-13T11:25:48Z
  file_id: '50480'
  file_name: ISWC 2023 TemporalFC-A Temporal Fact Checking approach over Knowledge
    Graphs.pdf
  file_size: 1944818
  relation: main_file
  success: 1
file_date_updated: 2024-01-13T11:25:48Z
has_accepted_license: '1'
intvolume: '     14265'
jel:
- C
keyword:
- temporal fact checking · ensemble learning · transfer learning · time-point prediction
  · temporal knowledge graphs
language:
- iso: eng
page: 465–483
place: Cham
project:
- _id: '410'
  grant_number: '860801'
  name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: The Semantic Web – ISWC 2023
publication_identifier:
  isbn:
  - '9783031472398'
  - '9783031472404'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer, Cham
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: '15921'
abstract:
- lang: eng
  text: "Ranking plays a central role in a large number of applications driven by
    RDF knowledge graphs. Over the last years, many popular RDF knowledge graphs have
    grown so large that rankings for the facts they contain cannot be computed directly
    using the currently common 64-bit platforms. In this paper, we tackle two problems:\r\nComputing
    ranks on such large knowledge bases efficiently and incrementally. First, we present
    D-HARE, a distributed approach for computing ranks on very large knowledge graphs.
    D-HARE assumes the random surfer model and relies on data partitioning to compute
    matrix multiplications and transpositions on disk for matrices of arbitrary size.
    Moreover, the data partitioning underlying D-HARE allows the execution of most
    of its steps in parallel.\r\nAs very large knowledge graphs are often updated
    periodically, we tackle the incremental computation of ranks on large knowledge
    bases as a second problem. We address this problem by presenting\r\nI-HARE, an
    approximation technique for calculating the overall ranking scores of a knowledge
    without the need to recalculate the ranking from scratch at each new revision.
    We evaluate our approaches by calculating ranks on the 3 × 10^9 and 2.4 × 10^9
    triples from Wikidata resp. LinkedGeoData. Our evaluation demonstrates\r\nthat
    D-HARE is the first holistic approach for computing ranks on very large RDF knowledge
    graphs. In addition, our incremental approach achieves a root mean squared error
    of less than 10E−7 in the best case. Both D-HARE\r\n and I-HARE are open-source
    and are available at: https://github.com/dice-group/incrementalHARE.\r\n"
author:
- first_name: Abdelmoneim Amer
  full_name: Desouki, Abdelmoneim Amer
  last_name: Desouki
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  last_name: Ngonga Ngomo
citation:
  ama: 'Desouki AA, Röder M, Ngonga Ngomo A-C. Ranking on Very Large Knowledge Graphs.
    In: <i>Proceedings of the 30th ACM Conference on Hypertext and Social Media  -
    HT ’19</i>. ACM; 2019:163-171. doi:<a href="https://doi.org/10.1145/3342220.3343660">10.1145/3342220.3343660</a>'
  apa: Desouki, A. A., Röder, M., &#38; Ngonga Ngomo, A.-C. (2019). Ranking on Very
    Large Knowledge Graphs. In <i>Proceedings of the 30th ACM Conference on Hypertext
    and Social Media  - HT ’19</i> (pp. 163–171). ACM. <a href="https://doi.org/10.1145/3342220.3343660">https://doi.org/10.1145/3342220.3343660</a>
  bibtex: '@inproceedings{Desouki_Röder_Ngonga Ngomo_2019, title={Ranking on Very
    Large Knowledge Graphs}, DOI={<a href="https://doi.org/10.1145/3342220.3343660">10.1145/3342220.3343660</a>},
    booktitle={Proceedings of the 30th ACM Conference on Hypertext and Social Media 
    - HT ’19}, publisher={ACM}, author={Desouki, Abdelmoneim Amer and Röder, Michael
    and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={163–171} }'
  chicago: Desouki, Abdelmoneim Amer, Michael Röder, and Axel-Cyrille Ngonga Ngomo.
    “Ranking on Very Large Knowledge Graphs.” In <i>Proceedings of the 30th ACM Conference
    on Hypertext and Social Media  - HT ’19</i>, 163–71. ACM, 2019. <a href="https://doi.org/10.1145/3342220.3343660">https://doi.org/10.1145/3342220.3343660</a>.
  ieee: A. A. Desouki, M. Röder, and A.-C. Ngonga Ngomo, “Ranking on Very Large Knowledge
    Graphs,” in <i>Proceedings of the 30th ACM Conference on Hypertext and Social
    Media  - HT ’19</i>, 2019, pp. 163–171.
  mla: Desouki, Abdelmoneim Amer, et al. “Ranking on Very Large Knowledge Graphs.”
    <i>Proceedings of the 30th ACM Conference on Hypertext and Social Media  - HT
    ’19</i>, ACM, 2019, pp. 163–71, doi:<a href="https://doi.org/10.1145/3342220.3343660">10.1145/3342220.3343660</a>.
  short: 'A.A. Desouki, M. Röder, A.-C. Ngonga Ngomo, in: Proceedings of the 30th
    ACM Conference on Hypertext and Social Media  - HT ’19, ACM, 2019, pp. 163–171.'
conference:
  end_date: 2019-09-20
  name: 30th ACM Conference on Hypertext and Social Media
  start_date: 2019-09-17
date_created: 2020-02-18T16:39:35Z
date_updated: 2022-01-06T06:52:41Z
department:
- _id: '574'
doi: 10.1145/3342220.3343660
keyword:
- Knowledge Graphs
- Ranking
- RDF
language:
- iso: eng
page: 163-171
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the 30th ACM Conference on Hypertext and Social Media  -
  HT '19
publication_identifier:
  isbn:
  - '9781450368858'
publication_status: published
publisher: ACM
status: public
title: Ranking on Very Large Knowledge Graphs
type: conference
user_id: '69382'
year: '2019'
...