Computing Semantic Association: Comparing Spreading Activation and Spectral Association for Ontology Learning

Gerhard Wohlgenannt, Stefan Belk, Matthias Schett

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

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Abstract

Spreading activation is a common method for searching semantic
or neural networks, it iteratively propagates activation for one
or more sources through a network { a process that is computationally
intensive. Spectral association is a recent technique to approximate
spreading activation in one go, and therefore provides very fast computation
of activation levels. In this paper we evaluate the characteristics
of spectral association as replacement for classic spreading activation in
the domain of ontology learning. The evaluation focuses on run-time performance
measures of our implementation of both methods for various
network sizes. Furthermore, we investigate differences in output, i.e. the
resulting ontologies, between spreading activation and spectral association.
The experiments confirm an excessive speedup in the computation
of activation levels, and also a fast calculation of the spectral association
operator if using a variant we called brute force. The paper concludes
with pros and cons and usage recommendations for the methods. (authors' abstract)
Original languageEnglish
Title of host publicationComputing Semantic Association: Comparing Spreading Activation and Spectral Association for Ontology Learning
Editors Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds.), MIWAI, Lecture Notes in Computer Science (LNCS) 8271
Place of PublicationKrabi, Thailand
PublisherSpringer
Pages317 - 328
Publication statusPublished - 1 Dec 2013

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102 not use (legacy)

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