@inproceedings{50795,
  author       = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngomo, Axel-Cyrille Ngonga}},
  booktitle    = {{The Semantic Web – ISWC 2022}},
  editor       = {{Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti, Valentina and Almeida, João Paulo A. and Takeda, Hideaki and Monnin, Pierre and Pirrò, Giuseppe and d’Amato, Claudia}},
  isbn         = {{978-3-031-19433-7}},
  keywords     = {{knowgraphs frockg raki 3dfed dice ngonga saleem roeder qudus}},
  pages        = {{462–480}},
  publisher    = {{Springer International Publishing}},
  title        = {{{HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-19433-7_27}},
  year         = {{2022}},
}

@inproceedings{32509,
  abstract     = {{ 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
which 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       = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web -- ISWC 2022}},
  editor       = {{Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti, Valentina}},
  isbn         = {{978-3-031-19433-7}},
  keywords     = {{fact checking · ensemble learning · knowledge graph veracit}},
  location     = {{Hanghzou, China}},
  pages        = {{462----480}},
  publisher    = {{Springer International Publishing}},
  title        = {{{HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs}}},
  doi          = {{10.1007/978-3-031-19433-7_27}},
  year         = {{2022}},
}