Abstract:
Formalization of natural language metaphors is a notorious problem in artificial intelligence and in other
overlapping domains. It is semantic vagueness that makes metaphors resistant to formulaic or algorithmic
descriptions. Great effort has been invested into modeling metaphors computationally but the issue remains
methodologically uncertain and needs further research. This paper works on a practical solution to the problem
how metaphorical meaning can be represented in a way suitable for computation. The research agenda of this
paper is interdisciplinary; it brings together an algebraic heuristic-driven theory for metaphors developed
in artificial intelligence and an applied theory of meaning that comes from cognitive linguistics. This agenda
postpones theoretical speculation and argument and is solely solution-focused, which contributes to the value
of this paper’s attempt to bridge the cognitive science disciplines whose compatibility, though declared, is seldom
demonstrated in a piece of practical research.
This paper works with metaphors of human emotions that are linguistically manifested in modern English
discourse. Emotions by virtue of their ineffability as qualia are rich in metaphorical conceptualizations and serve
the research agenda well. This paper in a meaningful way exposes and ranks designated properties of the
FEAR, SADNESS, HAPPINESS, and RELAXATION/SERENITY concepts and arranges these properties
into general-purpose ontologies that explicitly specify metaphorically preferred emotion conceptualizations
and are good candidates for computation. In prospect, this paper will account for some theoretical aspects of
the research and probe the algorithmic and repetitive nature of schemas that license metaphorical expressions
in natural language.
