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

Abstract

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.