RESEARCH GROUP

THEORETICAL

COMPUTER SCIENCE

Abstract In cognitive science, image schemas are identified as fundamental patterns of cognition. They are schematic prelinguistic conceptualisations of events and serve as conceptual building blocks for concepts. This paper proposes that image schemas can play an important role in computational concept invention, namely within the computational realisation of conceptual blending. We propose to build a library of formalised image schemas, and illustrate how they can guide the search for a base space in the concept invention work flow. Their schematic nature is captured by the idea of organising image schemas into families. Formally, they are represented as heterogeneous, interlinked theories.

Conceptual blending has been employed very successfully to understand the process of concept invention, studied particularly within cognitive psychology and linguistics. However, despite this influential research, within computational creativity little effort has been devoted to fully formalise these ideas and to make them amenable to computational techniques. Unlike other combination techniques, blending aims at creatively generating (new) concepts on the basis of input theories whose domains are thematically distinct but whose specifications share structural similarity based on a relation of analogy, identified in a generic space, called the base ontology. We here introduce the basic formalisation of conceptual blending, as sketched by the late Joseph Goguen, and discuss some of its variations. We illustrate the vast array of conceptual blends that may be covered by this approach and discuss the theoretical and conceptual challenges that ensue. Moreover, we show how the Distributed Ontology Language DOL can be used to declaratively specify blending diagrams of various shapes, and discuss in detail how the workflow and creative act of generating and evaluating a new, blended concept can be managed and computationally supported within Ontohub, a DOL-enabled theory repository with support for a large number of logical languages and formal linking constructs.

Conceptual blending has been employed very successfully to understand the process of concept invention, studied particularly within cognitive psychology and linguistics. However, despite this influential research, within computational creativity little effort has been devoted to fully formalise these ideas and to make them amenable to computational techniques. We here present the basic formalisation of conceptual blending, as sketched by the late Joseph Goguen, and show how the Distributed Ontology Language DOL can be used to declaratively specify blending diagrams. Moreover, we discuss in detail how the workflow and creative act of generating and evaluating a new, blended concept can be managed and computationally supported within Ontohub, a DOL-enabled theory repository with support for a large number of logical languages and formal linking constructs.

The theory of conceptual blending has been applied very successfully in cognitive science to explain the process of concept generation. In previous work, we have shown a formalisation and implementation of conceptual blending that is borrowing techniques from logic, ontological engineering, and algebraic specification. However, so far our attempts (and similar approaches in the literature) have been reconstructive; that is they show how a particular concept (e.g., houseboat) can be ‘discovered’ by computationally blending two carefully selected input spaces (e.g, a house and a boat) and a suitable base. This paper describes an attempt to take the next step and automate the concept generation based on a database of input spaces. Particularly, we attempt to create monsters from a library of animals formalised as OWL ontologies.

Motivation: Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined. Results: We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource.