The WWW has reached the stage where it can be looked upon as a gigantic information copying and distribution mechanism. But when the problem of distributing and copying information is essentially solved, where do we go next? There are a number of values that can be derived from the mesh, that also have immediate relevancy for the ML community. Goal of the workshop is to link these areas, and encourage cross-boundary thinking and working. Topics will be: (1) Machine learning and probabilistic modeling: Recommendation systems and knowledge extraction are two immediate applications, with research required for large scale inference, modeling languages, and efficient decision making. (2) Game theory and mechanism design: When a large number of contributors is involved, how can tasks and incentive structures be made such that the desired goal is achieved? Research is required for solving very large games, and for mechanism design under uncertainty. (3) Knowledge representation and reasoning: Large parts of the web are currently stored in an unstructured way, making linking and evaluating knowledge a complex problem. Open points are the difficulty of reasoning, the tradeoff between efficiency of reasoning and power of the representation, and reasoning under uncertainty. (4) Social networks and collective intelligence: How does information flow in the web? Who is reading what, who is in touch with whom? These networks need to be analyzed, modeled, and made amenable to reasoning. (5) Privacy preserving learning: What can be learned, and how can be learned, whilst only revealing a minimal set of information, or information that does not make users individually identifiable?