Structural analysis of relevance propagation models

Abstract

Relevance relations constitute the core of information retrieval. Topical ontologies, such as collaborative webpage classification projects, can provide a basis for identifying and analyzing such relations. New meaningful relevance relations can be automatically inferred from these ontologies by composing existing ones. In this work, several relevance propagation models are analyzed in terms of complex network theory. Structural properties such as Characteristic path length, Clustering coefficient and Degree distribution are computed over the models in order to understand the nature of each underlying network. This analysis raises interesting points about the Small-world and Scale-free structure of some relevance propagation models. Moreover, other connectivity and centrality measures are computed to gain additional insight into the topology of relevance. Finally, the analysis is complemented by providing visualizations of the k-core decomposition of different relevance propagation models. To illustrate the generalizability of the proposed methodology the analysis is carried out on an ontology from a different domain. The major theoretical implication of this analysis is the derivation of new instruments to typify semantic networks derived from relevance relations. The results can be exploited in a pragmatic way, as the parameters and properties derived by this analysis can serve as prior knowledge to algorithms for the automatic or semi-automatic construction of semantic networks.