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%A Peter D. Turney %O NRC-50738 %J Journal of Artificial Intelligence Research %T The Latent Relation Mapping Engine: Algorithm and Experiments %X Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance. %K analogy, metaphor, semantic relations, structure mapping, vector space models, analogical mapping, latent relational analysis %P 615-655 %V 33 %D 2008 %I AI Access Foundation %L cogprints6305