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The goal of this lecture is to present some of the fundamental algorithms on pertaining to fact checking. We will begin with a short introduction to knowledge graphs. Thereafter, we will delve into text-based and graph-based solutions to the fact checking problem. Finally, hybrid solutions will be presented.
Structure
The course consists of:
- A lecture
The course comprises 2 x 90min lectures
- A lab
Students are invited to gather practical experiences with SPARQL queries and knowledge-graphs. After that, they should extend the knowledge-graph-based Fact Checking system COPAAL.
Lab material
- DBPedia SPARQL endpoint
- COPAAL is an open-source knowledge-graph-based Fact Checking system
References
- Gerber, D., et al.: DeFacto–temporal and multilingual deep fact validation. In: Journal of Web Semantics, Volume 35, Part 2, pp. 85-101. Elsevier, 2015.
- Pasternack, J., Roth, D.: Knowing what to believe (when you already know something). In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 877-885. ACM, 2010.
- Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. In: Knowledge-Based Systems, Volume 104, pp. 123-133. Elsevier, 2016.
- Shiralkar, P., Flammini, A., Menczer, F., Ciampaglia, G.L.: Finding streams in knowledge graphs to support fact checking. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 859–864. IEEE, 2017.
- Syed, Z.H., Röder, M., Ngonga Ngomo, A.C.: Unsupervised Discovery of Corroborative Paths for Fact Validation. In: The Semantic Web — ISWC 2019, pp 630-646. Springer, 2019.
- Syed, Z.H., Röder, M., Ngonga Ngomo, A.C.: Factcheck: Validating rdf triples using textual evidence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1599–1602. ACM, 2018.