#5023. Structural analysis of relevance propagation models

July 2026publication date
Proposal available till 25-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Management Information Systems;
Information Systems and Management;
Software;
Artificial Intelligence;
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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. 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. 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.
Keywords:
Complex networks; Relevance propagation; Topic ontologies; Topological analysis

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