FaVEL: Fact Validation Ensemble Learning

conference paper FaVEL: Fact Validation Ensemble Learning general yes Umair Qudus author 833920000-0001-6714-8729 Michael Röder author 67199https://orcid.org/0000-0002-8609-8277 Franck Lionel Tatkeu Pekarou author Ana Alexandra Morim da Silva author 72108 Axel-Cyrille Ngonga Ngomo author 65716 MarcoRospocher editor 34 department 24th International Conference on Knowledge Engineering and Knowledge Management NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen project SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen project KnowGraphs: KnowGraphs: Knowledge Graphs at Scale project 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. https://ris.uni-paderborn.de/download/57240/57241/favel.pdf application/pdf 2024Amsterdam, Netherlands eng fact checkingensemble learningtransfer learningknowledge management. EKAW 2024 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. 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. 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. 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. @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} } 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. Qudus, Umair, et al. “FaVEL: Fact Validation Ensemble Learning.” EKAW 2024, edited by Marco Rospocher and Mehwish Alam, 2024. 572402024-11-19T14:12:49Z2025-09-11T09:48:12Z