Retrieving Semantically Distant Analogies

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Citation: Michael Wolverton. (1994) Retrieving Semantically Distant Analogies. In KSL-94-46, 1994.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Michael Wolverton
title Retrieving Semantically Distant Analogies
number KSL-94-46
institution Stanford University
year 1994
Bibtex more
note May 1994 STAN-CS-94-1515.
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abstract Techniques that have traditionally been useful for retrievingsame-domain analogies from small single-use knowledge bases, such asspreading activation and indexing on selected features, are inadequatefor retrieving cross-domain analogies from large multi-use knowledgebases. Blind or near-blind search techniques like spreadingactivation will be overwhelmed by combinatorial explosion as thesearch goes deeper into the KB. And indexing a large multi-use KB onsalient features is impractical, largely because a feature that may beuseful for retrieval in one task may be useless for another task. Thisthesis describes Knowledge-Directed Spreading Activation (KDSA), amethod for retrieving analogies in a large semantic network. KDSAuses task-specific knowledge to guide a spreading activation search toa case or concept in memory that meets a desired similarity condition.The thesis also describes a specific instantiation of this method forthe task of innovative design.KDSA has been validated in two ways. First, a theoretical model ofknowledge base search demonstrates that KDSA is tractable forretrieving semantically distant analogies under a wide range ofknowledge base configurations. Second, an implemented system that usesKDSA to find analogies for innovative design shows that the method isable to retrieve semantically distant analogies for a real task.Experiments with that system show trends as the knowledge base sizegrows that suggest the theoretical model's prediction of largeknowledge base tractability is accurate.

KSL Technical Report ID: KSL-94-46
Facts about Retrieving Semantically Distant AnalogiesRDF feed
Abstract Techniques that have traditionally been us … Techniques that have traditionally been useful for retrievingsame-domain analogies from small single-use knowledge bases, such asspreading activation and indexing on selected features, are inadequatefor retrieving cross-domain analogies from large multi-use knowledgebases. Blind or near-blind search techniques like spreadingactivation will be overwhelmed by combinatorial explosion as thesearch goes deeper into the KB. And indexing a large multi-use KB onsalient features is impractical, largely because a feature that may beuseful for retrieval in one task may be useless for another task. Thisthesis describes Knowledge-Directed Spreading Activation (KDSA), amethod for retrieving analogies in a large semantic network. KDSAuses task-specific knowledge to guide a spreading activation search toa case or concept in memory that meets a desired similarity condition.The thesis also describes a specific instantiation of this method forthe task of innovative design.KDSA has been validated in two ways. First, a theoretical model ofknowledge base search demonstrates that KDSA is tractable forretrieving semantically distant analogies under a wide range ofknowledge base configurations. Second, an implemented system that usesKDSA to find analogies for innovative design shows that the method isable to retrieve semantically distant analogies for a real task.Experiments with that system show trends as the knowledge base sizegrows that suggest the theoretical model's prediction of largeknowledge base tractability is accurate. geknowledge base tractability is accurate.
Author Michael Wolverton  +
Bibtype techreport  +
Has author Michael Wolverton  +
Has identifier KSL-94-46  +
Has publishing details 1994  +
Has title Retrieving Semantically Distant Analogies  +
Has where published KSL-94-46  +
Has year 1994  +
Institution Stanford University  +
Ksl tr id KSL-94-46  +
Note May 1994 STAN-CS-94-1515.
Number KSL-94-46  +
Process note NO  +
Title Retrieving Semantically Distant Analogies  +
Year 1994  +
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