The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact Verification Article Swipe
Yongyue Wang
,
Chunhe Xia
,
Chengxiang Si
,
Chongyu Zhang
,
Tianbo Wang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/electronics9091472
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/electronics9091472
Complex fact verification (FV) requires fusing scattered sequences and performing multi-hop reasoning over these composed sequences. Recently, by employing some FV models, knowledge is obtained from context to support the reasoning process based on pretrained models (e.g., BERT, XLNET), and this model outperforms previous out-of-the-art FV models. In practice, however, the limited training data cannot provide enough background knowledge for FV tasks. Once the background knowledge changed, the pretrained models’ parameters cannot be updated. Additionally, noise against common sense cannot be accurately filtered out due to the lack of necessary knowledge, which may have a negative impact on the reasoning progress. Furthermore, existing models often wrongly label the given claims as ‘not enough information’ due to the lack of necessary conceptual relationship between pieces of evidence. In the present study, a Dynamic Knowledge Auxiliary Graph Reasoning (DKAR) approach is proposed for incorporating external background knowledge in the current FV model, which explicitly identifies and fills the knowledge gaps between provided sources and the given claims, to enhance the reasoning ability of graph neural networks. Experiments show that DKAR put forward in this study can be combined with specific and discriminative knowledge to guide the FV system to successfully overcome the knowledge-gap challenges and achieve improvement in FV tasks. Furthermore, DKAR is adopted to complete the FV task on the Fake NewsNet dataset, showing outstanding advantages in a small sample and heterogeneous web text source.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics9091472
- https://www.mdpi.com/2079-9292/9/9/1472/pdf
- OA Status
- gold
- Cited By
- 4
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3084317880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3084317880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics9091472Digital Object Identifier
- Title
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The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact VerificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-09Full publication date if available
- Authors
-
Yongyue Wang, Chunhe Xia, Chengxiang Si, Chongyu Zhang, Tianbo WangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics9091472Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/9/9/1472/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/9/9/1472/pdfDirect OA link when available
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Computer science, Discriminative model, Knowledge graph, Artificial intelligence, Graph, Machine learning, Context (archaeology), Theoretical computer science, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2022: 3, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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