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Image Schema Combinations and Complex Events

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Hedblom, Maria M.
Kutz, Oliver
Peñaloza, Rafael
Guizzardi, Giancarlo

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Springer

Abstract

Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for conceptualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL , and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of ‘cracking an egg.’

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Hedblom, Maria M.; Kutz, Oliver; Peñaloza, Rafael; Guizzardi, Giancarlo (2019): Image Schema Combinations and Complex Events. KI - Künstliche Intelligenz: Vol. 33, No. 3. DOI: 10.1007/s13218-019-00605-1. Springer. PISSN: 1610-1987. pp. 279-291

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Commonsense reasoning, Egg cracking problem, Event structure, Image schemas, Knowledge representation, Ontology design patterns

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Number of citations to item: 21

  • Philipp Wicke, Tony Veale (2021): Are You Not Entertained? Computational Storytelling with Non-Verbal Interaction, In: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, doi:10.1145/3434074.3447159
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  • Mijin Im (2021): Integration of Content Instruction and Language Instruction in the Movie English Classroom: On the Basis of Embodiment, Image Schemas, and Metaphors, In: STEM Journal 2(22), doi:10.16875/stem.2021.22.2.14
  • Kaviya Dhanabalachandran, Vanessa Hassouna, Maria M. Hedblom, Michaela Küempel, Nils Leusmann, Michael Beetz (2021): Cutting Events, In: Proceedings of the 11th Knowledge Capture Conference, doi:10.1145/3460210.3493585
  • Stefano De Giorgis, Aldo Gangemi, Dagmar Gromann (2024): ImageSchemaNet: A knowledge graph for embodied commonsense knowledge, In: Semantic Web, doi:10.3233/sw-243084
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  • Maria M. Hedblom, Oliver Kutz, Rafael Peñaloza, Giancarlo Guizzardi (2019): Image Schema Combinations and Complex Events, In: KI - Künstliche Intelligenz 3(33), doi:10.1007/s13218-019-00605-1
  • Maria M. Hedblom, Fabian Neuhaus, Till Mossakowski (2024): The Diagrammatic Image Schema Language (DISL), In: Spatial Cognition & Computation 2(25), doi:10.1080/13875868.2024.2377284
  • Geng Cui, Yuho Chung, Ling Peng, Qiaofei Wang (2024): Clicks for money: Predicting video views through a sentiment analysis of titles and thumbnails, In: Journal of Business Research, doi:10.1016/j.jbusres.2024.114849
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  • Stefano De Giorgis, Aldo Gangemi, Dagmar Gromann (2024): ImageSchemaNet: A framester graph for embodied commonsense knowledge, In: Semantic Web 4(15), doi:10.3233/sw-223084
  • Marco Schorlemmer, Enric Plaza (2021): A uniform model of computational conceptual blending, In: Cognitive Systems Research, doi:10.1016/j.cogsys.2020.10.003
  • Amal Haddad Haddad (2022): Image Schemas and Image Schematic Complexes: Enhancing Neural Machine Translation Networks, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-15925-1_8
  • Hsueh-Man Shen (2024): Entering the Seemingly Unattainable Citadel of Laṅkā, In: Archives of Asian Art 1(74), doi:10.1215/00666637-11169073
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  • Fuyin Thomas Li (2024): Image schemas and (point)-to-point event model for the macro-event, In: Review of Cognitive Linguistics 1(24), doi:10.1075/rcl.00177.li
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