Auflistung nach Autor:in "Guizzardi, Giancarlo"
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- ZeitschriftenartikelImage Schema Combinations and Complex Events(KI - Künstliche Intelligenz: Vol. 33, No. 3, 2019) Hedblom, Maria M.; Kutz, Oliver; Peñaloza, Rafael; Guizzardi, GiancarloFormal 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.’
- Konferenz-AbstractOn Understanding the Value of Domain Modeling(EMISA 2022, 2022) Guizzardi, Giancarlo; Proper, Henderik A.
- KonferenzbeitragSemantic Models for Trustworthy Systems: A Hybrid Intelligence Augmentation Program(Modellierung 2024, 2024) Guizzardi, GiancarloCyber-human systems are formed by the coordinated interaction of human and computational components. In this talk, I will argue that these systems can only be designed as trustworthy systems if the interoperation between their components is meaning preserving. For that, we need to take the challenge of semantic interoperability between these components very seriously. I will discuss a notion of trustworthy semantic models and defend its essential role in addressing this challenge. Finally, I will advocate that engineering and evolving these semantic models as well as the languages in which they are produced require a hybrid intelligence augmentation program resting on a combination of techniques including formal ontology, logical representation and reasoning, crowdsourced validation, and automated approaches to mining and learning.