Summary of Hybridfc: a Hybrid Fact-checking Approach For Knowledge Graphs, by Umair Qudus et al.
HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs
by Umair Qudus, Michael Roeder, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed HybridFC approach leverages the strengths of five existing fact-checking categories for knowledge graphs, combining them in an ensemble learning setting to achieve superior prediction performance. Current text-based approaches are limited by manual feature engineering, while path-based and rule-based methods rely exclusively on knowledge graphs as background information. Embedding-based approaches struggle with low accuracy scores on current fact-checking tasks. The HybridFC 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. This open-source code is available at https://github.com/dice-group/HybridFC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to check if information in knowledge graphs is true or not. They looked at five different approaches that have been tried before and combined them together to make something better. The current methods have some limitations, like needing humans to create features for text-based approaches or relying only on the knowledge graph itself. This new approach does much better than the others, with a 0.14 to 0.27 improvement in accuracy. You can find the code they used at https://github.com/dice-group/HybridFC. |
Keywords
* Artificial intelligence * Embedding * Feature engineering * Knowledge graph
