An ontological analysis of vague motion verbs, with an application to event recognition
Tommaso D’Odorico · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2013
This research presents a methodology for the ontological formalisation of vague spatial concepts from natural language, with an application to the automatic recognition of event occurrences on video data. The main issue faced when defining concepts sourced from language is vagueness, related to the presence of ambiguities and borderline cases even in simple concepts such as ‘near’, ‘fast’, ‘big’, etc. Other issues specific to this semantic domain are saliency, granularity and uncertainty. In this work, the issue of vagueness in formal semantics is discussed and a methodology based on supervaluation semantics is proposed. This constitutes the basis for the formalisation of an ontology of vague spatial concepts based on classical logic, Event Calculus and supervaluation semantics. This ontology is structured in layers where high-level concepts, corresponding to complex actions and events, are inferred through mid-level concepts, corresponding to simple processes and properties of objects, and low-level primitive concepts, representing the most essential spatio-temporal characteristics of the real world. The development of ProVision, an event recognition system based on a logic-programming implementation of the ontology, demonstrates a practical application of the methodology. ProVision grounds the ontology on data representing the content of simple video scenes, leading to the inference of event occurrences and other high-level concepts. The contribution of this research is a methodology for the semantic characterisation of vague and qualitative concepts. This methodology addresses the issue of vagueness in ontologies and demonstrates the applicability of a supervaluationist approach to the formalisation of vague concepts. It is also proven to be effective towards solving a practical reasoning task, such as the event recognition on which this work focuses.