Inferring Event Causality in Films via Common Knowledge Corpora.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Human understanding of a movie plot is partially driven by our ability to reason about the causal relations between events. Thus, recognizing causal chains of events is a key requirement for computational models of movie understanding. In this paper, we propose to use available corpora of common-sense knowledge about human behavior for automatically inferring event causality in movie scenes. Our initial experiments with a dataset of annotated movie events and a corpus of human commonsense reasoning demonstrate that a) for 86% of movie events, there exist relevant commonsense rules and those rules can be used for predicting other movie events. b) in 70% of the cases, the consequences of the rules triggered by movie events can accurately or semi-accurately predict subsequent movie events.
These preliminary results indicate the potential of automated commonsense reasoning to detect the narrative structure in movies. Hence, the proposed method can contribute to the development of story-related video analytics tools, such as automatic video summarization and movie editing systems.
Original languageEnglish
Title of host publicationComputational Collective Intelligence ICCCI 2022
EditorsNgoc Thanh Nguyen, Yannis Manolopoulos
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-031-16014-1
ISBN (Print)978-3-031-16013-4
StatePublished - 21 Sep 2022
Event14th International Conference on Computational Collective Intelligence , ICCCI 2022 - Hammamet, Tunisia
Duration: 28 Sep 202230 Sep 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Computational Collective Intelligence , ICCCI 2022


  • Automated reasoning
  • Computational narrative understanding
  • Movie analytics
  • Event causality identification


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