Understanding Open-Ended Usages of Familiar Conceptual Metaphors: An Approach and Artificial Intelligence System (Technical Report CSRP–01–05)

We present and evaluate an approach to the reasoning needed to handle a broad class of metaphorical utterances, and a computer program (ATT-Meta) partially implementing and further specifying that approach. The approach emanates from artificial intelligence but is offered also for consideration by cognitive scientists generally. The utterances of interest are ones that (a) rest on conceptual metaphors that are familiar to the understander but (b) transcend the mappings in the conceptual metaphors by using concepts not handled by the mappings. Our approach advocates possibly-extensive inferencing in the terms of the source (vehicle) domains of the conceptual metaphors, while avoiding as far as possible the extension of the mappings to deal with the concepts they do not handle. The general approach is similar in flavor to those of a small number of other metaphor researchers, but we provide a more extensive analysis, additional principles and a more thorough-going implementation. The approach contains a number of “view-neutral mapping adjuncts,” which are default mapping principles that enable important source-domain aspects to be mapped to the target domain, independently of which specific metaphorical views are in play. Many discussions of metaphor appear to assume that such mapping actions occur, but rarely address them systematically and explicitly. In addition, in the approach, a conceptual metaphor can consist not only of a between-domain mapping but also of special, ancillary assumptions that serve to enrich the source domain with specific details needed by the metaphors. The implemented system supports ancillary assumptions but currently only has a preliminary handling of view-neutral mapping adjuncts.