---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Umair
      foaf_name: Qudus, Umair
      foaf_surname: Qudus
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Röder, Michael
      foaf_surname: Röder
  - foaf_Person:
      foaf_givenName: Sabrina
      foaf_name: Kirrane, Sabrina
      foaf_surname: Kirrane
  - foaf_Person:
      foaf_givenName: Axel-Cyrille Ngonga
      foaf_name: Ngomo, Axel-Cyrille Ngonga
      foaf_surname: Ngomo
  bibo_doi: 10.1007/978-3-031-47240-4_25
  bibo_volume: 14265
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0302-9743
  - http://id.crossref.org/issn/1611-3349
  - http://id.crossref.org/issn/9783031472398
  - http://id.crossref.org/issn/9783031472404
  dct_language: eng
  dct_publisher: Springer, Cham@
  dct_subject:
  - temporal fact checking · ensemble learning · transfer learning · time-point prediction
    · temporal knowledge graphs
  dct_title: 'TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs@'
...