Grammar-based random walkers in semantic networks

Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most “central” in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics h...

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Bibliographic Details
Published in:Knowledge-based systems 2008-10, Vol.21 (7), p.727-739
Main Author: Rodriguez, Marko A.
Format: Article
Language:English
Subjects:
Eigenvector centrality
PageRank
Primary eigenvector
RDF/RDFS
Semantic networks
Semantic Web
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Summary:Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most “central” in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user-defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2008.03.030