In view of the dependence of metaphor on context and extensive knowledge, and the myriad difficulties still confronting all aspects of language understanding, it is not surprising that no general system for processing metaphor in context exists, let alone for using metaphor creatively. Still, Martin's MIDAS program was able to interpret a variety of metaphors in the context of a language-based Unix adviser, relying on knowledge about the domain and about metaphorical mapping, hand-coded in the KODIAK knowledge representation language. Also, several other programs have demonstrated a capacity to analyze or generate various examples of metaphors, including the Structure Mapping Engine (SME) (Falkenhainer et al.  1989), Met* (Fass 1991), ATT-Meta (Barnden 2001), KARMA (Narayanan 1997) and others. More recently, Veale and Hao (2008) undertook an empirical study of a slippage-based approach to metaphor, using attributes collected from WordNet and the web. In a similar spirit, but taking SME as his inspiration, Turney (2008) implemented a “Latent Relation Mapping Engine” (LRME) to find the best mapping between the elements of two potentially comparable descriptions (of equal size); the idea is to use web-based co-occurrence statistics to gauge not only the attribute similarity of any two given concepts (such as electron and planet) but also the relational similarity of any two given pairs of concepts (such as electron:nucleus and planet:sun), using these as metrics in optimizing the mapping.