A Fast Mapper as a Foundation for Forthcoming Conceptual Blending Experiments



Algorithms for finding analogies as mappings between pairs of concepts are fundamental to some implementations of Conceptual Blending (CB), a theory which has been suggested as explaining some cognitive processes behind the creativity phenomenon. When analogies are defined as sub-isomorphisms of semantic graphs, we find ourselves with a NP-complete problem. In this paper we propose and compare a new high performance stochastic mapper that efficiently handles semantic graphs containing millions of relations between concepts, while outputting in real-time analogy mappings ready for use by another algorithm, such as a computational system based on CB theory.


Computational Analogy


Computational Analogy


26th International Conference on Case-Based Reasoning, June 2018

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