TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs Qudus, Umair Röder, Michael Kirrane, Sabrina Ngomo, Axel-Cyrille Ngonga R. Payne, Terry Presutti, Valentina Qi, Guilin Poveda-Villalón, María Stoilos, Giorgos Hollink, Laura Kaoudi, Zoi Cheng, Gong Li, Juanzi temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs ddc:006 Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC. Springer, Cham 2023 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/50479 Qudus U, Röder M, Kirrane S, Ngomo A-CN. TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et al., eds. <i>The Semantic Web – ISWC 2023</i>. Vol 14265. Lecture Notes in Computer Science. Springer, Cham; 2023:465–483. doi:<a href="https://doi.org/10.1007/978-3-031-47240-4_25">10.1007/978-3-031-47240-4_25</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-47240-4_25 info:eu-repo/semantics/altIdentifier/issn/0302-9743 info:eu-repo/semantics/altIdentifier/issn/1611-3349 info:eu-repo/semantics/altIdentifier/isbn/9783031472398 info:eu-repo/semantics/altIdentifier/isbn/9783031472404 info:eu-repo/grantAgreement/EC/860801 info:eu-repo/semantics/closedAccess