There is generalagreementthat the quality of Machine Learning and Kno- edgeDiscoveryoutputstronglydependsnotonlyonthequalityofsourcedata andsophisticationoflearningalgorithms, butalsoonadditional, task/domain speci?c input provided by domain experts for the particular session. There is however less agreement on whether, when and how such input can and should e?ectively be formalized and reused as explicit prior knowledge. In the ?rst ofthe two parts into which the book is divided, we aimed to - vestigate current developments and new insights on learning techniques that exploit prior knowledge and on promising application areas. With respect to application areas, experiments on bio-informatics / medical and Web data environments are described. This part comprises a selection of extended c- tributionstothe workshopPrior Conceptual Knowledge inMachine Learning and Knowledge Discovery (PriCKL), held at ECML/PKDD 2007 18th - ropean Conference on Machine Learning and 11th European Conference on PrinciplesandPracticeofKnowledgeDiscoveryinDatabases).Theworkshop is part of the activities of the "SEVENPRO - Semantic Virtual Engineering for Product Design" project of the European 6th Framework Programme. The second part of the book has been motivated by the speci?cation of Web 2.0. We observe Web 2.0 as a powerful means of promoting the Web as a social medium, stimulating interpersonal communication and fostering the sharing of content, information, semantics and knowledge among people. Chapters are authored by participants to the workshop Web Mining 2.0, heldatECML/PKDD2007.Theworkshophostedresearchontheroleofweb mininginandfortheWeb2.0.Itispartoftheactivitiesoftheworkinggroups "UbiquitousData-InteractionandDataCollection"and"HumanComputer Interaction and Cognitive Modelling" of the Coordination Action "KDubiq - Knowledge Discovery in Ubiquitous Environments" of the European 6th Framework Programme.