MultiRank: Reputation Ranking for Generic Semantic Social Networks

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Citation: Xixi Luo,Joshua Shinavier. (2009) MultiRank: Reputation Ranking for Generic Semantic Social Networks. In 1st International Workshop on Motivation and Incentives on the Web, April,2009. (Download)

Publication inproceedings ( Edit )
type Workshop Paper
bibtype inproceedings
Bibtex basics
author Xixi Luo;Joshua Shinavier
title MultiRank: Reputation Ranking for Generic Semantic Social Networks
booktitle 1st International Workshop on Motivation and Incentives on the Web
address Madrid, Spain
year 2009
month April
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abstract This paper presents a technique for calculating “reputation” or influence of users and artifacts in semantic social networks: in particular, as an incentive mechanism to encourage reuse of complex resources such as ontologies. Adapting the PageRank algorithm to the relational schemas of typical social network applications, this technique allows the programmer first to define via minimal rules the ways in which reputations of users and artifacts are likely to influence one another, then to obtain a mechanical, global ranking which reflects those rules in combination with the graph structure of the network. The mapping of multi-way relations such as usage and annotation to the binary-relational domain of PageRank is illustrated using the Actor-Concept-Instance model of ontologies. A lightweight software implementation, currently under development, will provide a convenient way to add reputation-based functionality to Java-based community applications.
pdf url http://tw.rpi.edu/portal/Image:MultiRank.pdf
Facts about MultiRank: Reputation Ranking for Generic Semantic Social NetworksRDF feed
Abstract This paper presents a technique for calcul … This paper presents a technique for calculating “reputation” or influence of users and artifacts in semantic social networks: in particular, as an incentive mechanism to encourage reuse of complex resources such as ontologies. Adapting the PageRank algorithm to the relational schemas of typical social network applications, this technique allows the programmer first to define via minimal rules the ways in which reputations of users and artifacts are likely to influence one another, then to obtain a mechanical, global ranking which reflects those rules in combination with the graph structure of the network. The mapping of multi-way relations such as usage and annotation to the binary-relational domain of PageRank is illustrated using the Actor-Concept-Instance model of ontologies. A lightweight software implementation, currently under development, will provide a convenient way to add reputation-based functionality to Java-based community applications. lity to Java-based community applications.
Address Madrid, Spain  +
Author Xixi Luo  +, and Joshua Shinavier  +
Bibtype inproceedings  +
Booktitle 1st International Workshop on Motivation and Incentives on the Web  +
Has author Xixi Luo  +, and Joshua Shinavier  +
Has identifier TW-2009-09  +
Has publishing details April,2009  +
Has title MultiRank: Reputation Ranking for Generic Semantic Social Networks  +
Has tr id TW-2009-09  +
Has url http://tw.rpi.edu/portal/Image:MultiRank.pdf  +
Has where published 1st International Workshop on Motivation and Incentives on the Web  +
Has year 2009  +
Month April  +
Pdf url http://tw.rpi.edu/portal/Image:MultiRank.pdf  +
Title MultiRank: Reputation Ranking for Generic Semantic Social Networks  +
Year 2009  +
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