Probabilistic modelling
Stochastic processes
Concepts Programmng Languages (7th Edition)
Concepts Programmng Languages (7th Edition)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
ACM SIGKDD Explorations Newsletter
Model Driven Architecture and Ontology Development
Model Driven Architecture and Ontology Development
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
An algorithm to determine peer-reviewers
Proceedings of the 17th ACM conference on Information and knowledge management
Automatic metadata generation using associative networks
ACM Transactions on Information Systems (TOIS)
Grammar-based geodesics in semantic networks
Knowledge-Based Systems
Knowledge-Based Systems
A novel measure of edge centrality in social networks
Knowledge-Based Systems
Hi-index | 0.00 |
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.