RADAR: Finding Analogies Using Attributes of Structure

DOI: 10.1007/3-540-45750-X_3


RADAR is a model of analogy retrieval that employs the principle of systematicity as its primary retrieval cue. RADAR was
created to address the current bias toward semantics in analogical retrieval models, to the detriment of structural factors.
RADAR recalls 100% of structurally identical domains. We describe a technique based on “derived attributes” that captures structural descriptions
of the domain’s representation rather than domain contents. We detail their use, recall and performance within RADAR through empirical evidence. We contrast
RADAR with existing models of analogy retrieval. We also demonstrate that RADAR can retrieve both semantically related and
semantically unrelated domains, even without a complete target description, which plagues current models.

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Available from: Diarmuid P. O'Donoghue, Mar 28, 2014
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    • "O'Donoghue argues that structural similarity is more important for the identification of good analogical candidates than semantic similarity. But, because the most accurate way to assess structural similarity can only be done using structural mapping, which is computational expensive, he has used structural properties to assess the structural similarity, which are fast to assess [21] "
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