I read the paper:
T. Veale and Y. Hao. 2008. A ﬂuid knowledge representation for understanding and generating creative metaphors. In Proceedings of COLING 2008, pages 945–952, Manchester, UK.
The goal of this paper is metaphor interpretation and generation (ambitious!). Michael gives a good overview of their approach which I would breakdown into 3 steps:
1. Extract facts
2. Link facts
3. Use knowledge representation to interpret metaphors
#1 seems straightforward and they accomplish it using WordNet and the web. They also have empirical results demonstrating the quality of their facts.
#2 seems more difficult and they give some small amount of details of how they identify closely related facts using semantic relations in WordNet. #3 they mostly explain by examples linking together two seemingly unrelated nouns like "Pope" and "Don (Crime Father)". It seems to me that the algorithm can be thought of as constructing a graph using some heuristic rules and then finding a path (any path?) through the graph from A to B. Its unclear to me how this could be used to interpret a new metaphor and the authors don't seem to address this directly. This also seems to contrast with earlier cited work which is also called a slipnet and is referred to as a "probabilistic network". As far as I can tell, there is nothing stochastic about their approach. I briefly endeavored to read the earlier work (Hofstadter, 1994) but failed due to the fact that it is 80 pages.
They also don't have any way of measuring the quality of their interpretations which, in fairness, seems like a difficult task.