[{"popular_science":"1","type":"journal_article","status":"public","_id":"61123","user_id":"83392","department":[{"_id":"574"}],"article_type":"original","article_number":"3749838","file_date_updated":"2025-09-11T09:26:29Z","publication_status":"published","publication_identifier":{"issn":["0360-0300","1557-7341"]},"has_accepted_license":"1","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>","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>.","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>","short":"U. Qudus, M. Röder, M. Saleem, A.-C. Ngonga Ngomo, ACM Computing Surveys 58 (2025).","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} }","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>."},"intvolume":"        58","date_updated":"2025-09-11T09:30:28Z","oa":"1","author":[{"last_name":"Qudus","orcid":"0000-0001-6714-8729","id":"83392","full_name":"Qudus, Umair","first_name":"Umair"},{"first_name":"Michael","full_name":"Röder, Michael","id":"67199","last_name":"Röder","orcid":"https://orcid.org/0000-0002-8609-8277"},{"first_name":"Muhammad","last_name":"Saleem","full_name":"Saleem, Muhammad"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"volume":58,"main_file_link":[{"url":"https://dl.acm.org/doi/pdf/10.1145/3749838","open_access":"1"}],"doi":"10.1145/3749838","publication":"ACM Computing Surveys","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>"}],"file":[{"creator":"uqudus","date_created":"2025-09-11T09:26:29Z","date_updated":"2025-09-11T09:26:29Z","file_id":"61195","access_level":"closed","file_name":"3749838.pdf","file_size":1062387,"content_type":"application/pdf","relation":"main_file","success":1}],"external_id":{"unknown":["10.1145/3749838"]},"ddc":["006"],"keyword":["fact checking","knowledge graphs","fact-checkers","check worthiness","evidence retrieval","trust","veracity."],"language":[{"iso":"eng"}],"quality_controlled":"1","year":"2025","publisher":"Association for Computing Machinery (ACM)","date_created":"2025-09-03T15:46:43Z","title":"Fact Checking Knowledge Graphs -- A Survey"},{"date_created":"2025-10-28T10:02:40Z","publisher":"ACM","title":"Parameter Averaging in Link Prediction","year":"2025","language":[{"iso":"eng"}],"keyword":["Knowledge Graphs","Embeddings","Ensemble Learning"],"ddc":["000"],"publication":"Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)","file":[{"relation":"main_file","content_type":"application/pdf","file_size":837462,"file_name":"public.pdf","file_id":"62008","access_level":"open_access","date_updated":"2025-10-28T10:02:13Z","creator":"rupezzz","date_created":"2025-10-28T10:02:13Z"}],"abstract":[{"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.","lang":"eng"}],"author":[{"first_name":"Rupesh","last_name":"Sapkota","id":"89326","full_name":"Sapkota, Rupesh"},{"first_name":"Caglar","full_name":"Demir, Caglar","last_name":"Demir"},{"full_name":"Sharma, Arnab","last_name":"Sharma","first_name":"Arnab"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"oa":"1","date_updated":"2025-12-04T09:15:07Z","doi":"https://doi.org/10.1145/3731443.3771365","conference":{"end_date":"2025-12-10","location":"Dayton, OH, USA","name":"Knowledge Capture Conference 2025","start_date":"2025-12-10"},"main_file_link":[{"url":"https://papers.dice-research.org/2025/KCAP_ASWA/public.pdf"}],"has_accepted_license":"1","citation":{"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.","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} }","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>.","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>","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>","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>.","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>."},"place":"Dayton, OH, USA","department":[{"_id":"574"}],"user_id":"89326","_id":"62007","project":[{"_id":"285","name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"file_date_updated":"2025-10-28T10:02:13Z","type":"conference","status":"public"},{"status":"public","editor":[{"full_name":"R. Payne, Terry","last_name":"R. Payne","first_name":"Terry"},{"last_name":"Presutti","full_name":"Presutti, Valentina","first_name":"Valentina"},{"first_name":"Guilin","last_name":"Qi","full_name":"Qi, Guilin"},{"first_name":"María","last_name":"Poveda-Villalón","full_name":"Poveda-Villalón, María"},{"last_name":"Stoilos","full_name":"Stoilos, Giorgos","first_name":"Giorgos"},{"first_name":"Laura","full_name":"Hollink, Laura","last_name":"Hollink"},{"first_name":"Zoi","last_name":"Kaoudi","full_name":"Kaoudi, Zoi"},{"full_name":"Cheng, Gong","last_name":"Cheng","first_name":"Gong"},{"last_name":"Li","full_name":"Li, Juanzi","first_name":"Juanzi"}],"type":"conference","file_date_updated":"2024-01-13T11:25:48Z","series_title":" Lecture Notes in Computer Science","user_id":"83392","department":[{"_id":"34"}],"project":[{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","grant_number":"860801"}],"_id":"50479","citation":{"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>.","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>.","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>","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.","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} }","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>."},"jel":["C"],"page":"465–483","intvolume":"     14265","place":"Cham","publication_status":"published","has_accepted_license":"1","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031472398","9783031472404"]},"doi":"10.1007/978-3-031-47240-4_25","conference":{"name":"The Semantic Web – ISWC 2023","start_date":"2023-11-06","end_date":"2023-11-10","location":"Athens, Greece"},"author":[{"full_name":"Qudus, Umair","last_name":"Qudus","first_name":"Umair"},{"last_name":"Röder","full_name":"Röder, Michael","first_name":"Michael"},{"full_name":"Kirrane, Sabrina","last_name":"Kirrane","first_name":"Sabrina"},{"last_name":"Ngomo","full_name":"Ngomo, Axel-Cyrille Ngonga","first_name":"Axel-Cyrille Ngonga"}],"volume":14265,"date_updated":"2024-01-13T11:48:28Z","file":[{"relation":"main_file","success":1,"content_type":"application/pdf","access_level":"closed","file_id":"50480","file_name":"ISWC 2023 TemporalFC-A Temporal Fact Checking approach over Knowledge Graphs.pdf","file_size":1944818,"date_created":"2024-01-13T11:25:48Z","creator":"uqudus","date_updated":"2024-01-13T11:25:48Z"}],"abstract":[{"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.","lang":"eng"}],"publication":"The Semantic Web – ISWC 2023","language":[{"iso":"eng"}],"ddc":["006"],"keyword":["temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs"],"year":"2023","title":"TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs","date_created":"2024-01-13T11:22:15Z","publisher":"Springer, Cham"},{"type":"conference","publication":"Proceedings of the 30th ACM Conference on Hypertext and Social Media  - HT '19","status":"public","abstract":[{"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","lang":"eng"}],"user_id":"69382","department":[{"_id":"574"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"15921","language":[{"iso":"eng"}],"keyword":["Knowledge Graphs","Ranking","RDF"],"publication_status":"published","publication_identifier":{"isbn":["9781450368858"]},"citation":{"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>","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.","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} }","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>.","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.","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>.","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>"},"page":"163-171","year":"2019","date_created":"2020-02-18T16:39:35Z","author":[{"last_name":"Desouki","full_name":"Desouki, Abdelmoneim Amer","first_name":"Abdelmoneim Amer"},{"last_name":"Röder","full_name":"Röder, Michael","first_name":"Michael"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"publisher":"ACM","date_updated":"2022-01-06T06:52:41Z","doi":"10.1145/3342220.3343660","conference":{"start_date":"2019-09-17","name":"30th ACM Conference on Hypertext and Social Media","end_date":"2019-09-20"},"title":"Ranking on Very Large Knowledge Graphs"}]