We are pleased to announce the following keynote speakers:

  • Professor Keith Stenning, University of Edinburgh

    Diagrams and nonmonotonic logic: what is the cognitive relation?
    Keith Stenning is a cognitive scientist working on human reasoning and decision. He has sought to understand how formalisms such as logics can further the business of experimental analysis of human reasoning. His PhD applied logical model theory to the data of discourse processing, and showed how the conventions of exposition (say story telling) conspire to allow the hearer to construct a unique model of the speaker’s discourse which is the basis of their subsequent inferences. Soon after preferred model semantics made Logic Programming (LP) into a nonmonotonic logic ideal for modelling this cooperative process.

    Turning to diagrammatic reasoning to gain perspective on language processing, Seeing Reason (2002) contrasted diagrammatic and sentential systems of teaching logic to analyse what it is that students learn when they learn elementary logic. Returning to discourse, Human Reasoning and Cognitive Science (2008) (with Michiel van Lambalgen) provided evidence that more than one formalism is required to capture the qualitatively different kinds of reasoning that people engage in for the many purposes they pursue.
    Proving a theorem is not telling a story, yet with the richness of modern logic, it is possible to find logics that capture the essences of both activities.

    Recently, he has worked on integrating LP with decision heuristics to provide a nonprobabilistic model of semantic memory involvement in nonmonotonic reasoning (Stenning, Martignon and Varga 2017).

    Somewhere along the way, he became a Foreign Fellow of the Royal Dutch National Academy, and a Distinguished Fellow of the Cognitive Science Society.

  • Pascal Hitzler, Wright State University.
    Modular Ontologies As A Bridge Between Human Conceptualization and Data. Ontologies can be viewed as the middle layer between pure human conceptualization and machine readability. However, they have not lived up to their promises so far. Most ontologies are too tailored to specific data and use-cases. By making sometimes strong, or sometimes too weak, ontological commitments, many existing ontologies do not adequatly reflect human conceptualizations. As a result, sharing and reuse
    of ontologies is greatly inhibited. In order to more effectively preserve this notion of human conceptualization, an ontology should be designed with modularity and extensibility in mind. A modular ontology thus may act as a bridge between human conceptualization and data.