Spontaneously retrieving analogies from
presented problem data is an important phase of
analogical reasoning, influencing many related
cognitive processes. Existing models have
focused on semantic similarity, but structural
similarity is also a necessary requirement of any
analogical comparison. We present a new
technique for performing structure based analogy
retrieval. This is founded upon derived attributes
that explicitly encode elementary structural
qualities of a domains representation. Crucially,
these attributes are unrelated to the semantic
content of the domain information, and encode
only its structural qualities. We describe a
number of derived attributes and detail the
computation of the corresponding attribute
values. We examine our models operation,
detailing how it retrieves both semantically
related and unrelated domains. We also present a
comparison of our algorithms performance with
existing models, using a structure rich but
semantically impoverished domai