Retrieving semantically distant analogies

Techniques that have traditionally been useful for retrieving same-domain analogies from small single-use knowledge bases, such as spreading activation and indexing on selected features, are inadequate for retrieving cross-domain analogies from large multi-use knowledge bases. Blind or near-blind search techniques like spreading activation will be overwhelmed by combinatorial explosion as the search goes deeper into the KB. And indexing a large multi-use KB on salient features is impractical, largely because a feature that may be useful for retrieval in one task may be useless for another task. This thesis describes Knowledge-Directed Spreading Activation (KDSA), a method for retrieving analogies in a large semantic network. KDSA uses task-specific knowledge to guide a spreading activation search to a case or concept in memory that meets a desired similarity condition. The thesis also describes a specific instantiation of this method for the task of innovative design. KDSA has been validated in two ways. First, a theoretical model of knowledge base search demonstrates that KDSA is tractable for retrieving semantically distant analogies under a wide range of knowledge base configurations. Second, an implemented system that uses KDSA to find analogies for innovative design shows that the method is able to retrieve semantically distant analogies for a real task. Experiments with that system show trends as the knowledge base size grows that suggest the theoretical model's prediction of large knowledge base tractability is accurate.