HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs
This addresses fact-checking in knowledge graphs for AI and data science applications, but it is incremental as it combines existing categories of approaches.
The paper tackles the problem of predicting the veracity of assertions in knowledge graphs by proposing a hybrid ensemble approach, which outperforms the state of the art by 0.14 to 0.27 in AUC on the FactBench dataset.
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.