HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs
<|reference_start|>HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs: 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.<|reference_end|>
@article{qudus2024hybridfc:,
    title={HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs},
    author={Umair Qudus, Michael Roeder, Muhammad Saleem, and Axel-Cyrille Ngonga
  Ngomo},
    journal={The Semantic Web . ISWC 2022. ISWC 2022. Lecture Notes in Computer
  Science, vol 13489. Springer, Cham},
    year={2024},
    doi={10.1007/978-3-031-19433-7_27},
    archivePrefix={arXiv},
    eprint={2409.06692},
    primaryClass={cs.LG cs.AI cs.DB}
}