@inproceedings{57240,
  abstract     = {{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       = {{Qudus, Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva, Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{EKAW 2024}},
  editor       = {{Rospocher, Marco}},
  keywords     = {{fact checking, ensemble learning, transfer learning, knowledge management.}},
  location     = {{Amsterdam, Netherlands}},
  title        = {{{FaVEL: Fact Validation Ensemble Learning}}},
  year         = {{2024}},
}

@inproceedings{57278,
  author       = {{Morim da Silva, Ana Alexandra and Srivastava, Nikit and Moteu Ngoli, Tatiana and Röder, Michael and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Benchmarking Low-Resource Machine Translation Systems}}},
  doi          = {{10.18653/v1/2024.loresmt-1.18}},
  year         = {{2024}},
}