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Added by benbanbun on 2010-11-17 10:16:58
Link mining: A survey
Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.
Lise Getoor, Christopher P. Diehl
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Added by benbanbun on 2010-11-11 11:13:53
A Graph Analysis of the Linked Data Cloud
The Linked Data community is focused on integrating Resource Description Framework (RDF)
data sets into a single unified representation known as the Web of Data. The Web of Data can be
traversed by both man and machine and shows promise as the de facto standard for integrating
data world wide much like the World Wide Web is the de facto standard for integrating documents.
On February 27th of 2009, an updated Linked Data cloud visualization was made publicly available.
This visualization represents the various RDF data sets currently in the Linked Data cloud and their
interlinking relationships. For the purposes of this article, this visual representation was manually
transformed into a directed graph and analyzed.
Marko A. Rodriguez
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Added by benbanbun on 2010-11-09 10:55:43
Finding the Achilles Heel of the Web of Data: using network analysis for link-recommendation
The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, e-commerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustness-measure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.
Christophe Gueret, Paul Groth, Frank van Harmelen, Stefan Schlobach
