Graph mining refers to extracting knowledge from massive graphs. The data sets of telephone calls we see at AT&T can be viewed as a single graph, with several hundred million phone numbers as nodes, and calls between phone numbers as edges. It is a giant social network, like an internet connections graph or a rich citation network.
There are several semantic sources that can be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web usage data. Most of them are related to user generated content (UGC) or what is called today the Web 2.0. In this talk we show several applications of mining the wisdom of crowds behind UGC to improve search. We will show live demos to find relations in the Wikipedia or to improve image search as well as our current research in the topic. Our final goal is to produce a virtuous data feedback circuit to leverage the Web itself.
?. Proceedings of the Workshop on Third Generation Data Mining: Towards Service-oriented Knowledge Discovery at ECML/PKDD 2008, (2008)Published online..
A. Abraham, und V. Ramos. Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, Seite 1384--1391. Canberra, IEEE Press, (8-12 December 2003)