Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)
Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)
Ronald Denaux, Martino Mensio, Jose Manuel Gomez-Perez, Harith Alani
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Sister Conferences Best Papers. Pages 4760-4764.
https://doi.org/10.24963/ijcai.2021/646
This paper summarises work where we combined semantic web technologies with deep learning systems to obtain state-of-the art explainable misinformation detection. We proposed a conceptual and computational model to describe a wide range of misinformation detection systems based around the concepts of credibility and reviews. We described how Credibility Reviews (CRs) can be used to build networks of distributed bots that collaborate for misinformation detection which we evaluated by building a prototype based on publicly available datasets and deep learning models.
Keywords:
Knowledge Representation and Reasoning: Semantic Web
AI Ethics, Trust, Fairness: Societal Impact of AI
AI Ethics, Trust, Fairness: Explainability
Natural Language Processing: NLP Applications and Tools