2008/03/31 by Marko A. Rodriguez · 1 citation
Computer Science · #Advanced Graph Neural Networks #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services #cs.AI #cs.DS
paper · pdf · doi:10.1016/j.knosys.2008.03.030
published as Rodriguez, M.A., "Grammar-Based Random Walkers in Semantic Networks", Knowledge-Based Systems, volume 21, issue 7, pages 727-739, ISSN: 0950-7051, Elsevier, October 2008 · First draft of manuscript originally written in November 2006
openalex publication_date 2008/04/09 · arxiv created 2008/09/10 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.