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

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)