Image Schema Combinations and Complex Events
2019 (English)In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 33, no 3, p. 279-291Article in journal (Refereed) Published
Abstract [en]
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’.
Place, publisher, year, edition, pages
Springer, 2019. Vol. 33, no 3, p. 279-291
Keywords [en]
Commonsense reasoning, Egg cracking problem, Event structure, Image schemas, Knowledge representation, Ontology design patterns, Image segmentation, Ontology, Complex events, Cracking problem, Design Patterns, Event structures, Image schemata, Knowledge-representation, Ontology design, Ontology design pattern
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-65640DOI: 10.1007/s13218-019-00605-1ISI: 000479274300008Scopus ID: 2-s2.0-85087153156OAI: oai:DiVA.org:hj-65640DiVA, id: diva2:1884402
2024-07-162024-07-162024-07-16Bibliographically approved