Grammar-Based Random Walkers in Semantic Networks

TL;DR

This paper introduces a grammar-constrained random walk framework for calculating meaningful centrality metrics like eigenvector centrality and PageRank in semantic networks, addressing the challenge of subjective relationship importance.

Contribution

It proposes a novel method that incorporates user-defined grammars into random walk models to derive semantically relevant vertex rankings in semantic networks.

Findings

Enables calculation of centrality metrics respecting semantic constraints

Adapts random walk models for semantic network analysis

Applies framework within RDF and Semantic Web context

Abstract

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.