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Collaborative tagging systems like Flickr and del.icio.us provide centralized content annotation and sharing which is simple to use and attracts many people. A combination of tagging with peer-to-peer systems overcomes typical limitations of centralized systems, however, decentralization also hampers the efficient computation of global statistics facilitating user navigation. We present Tagster, a peer-to-peer based tagging system that provides a solution to this challenge. We describe a typical scenario that demonstrates the advantages of distributed content sharing with Tagster. [ABSTRACT FROM AUTHOR]

This paper describes an efficient method to construct reliable machine learning applications in peer-to-peer (P2P) networks by building ensemble based meta methods. We consider this problem in the context of distributed Web exploration applications like focused crawling. Typical applications are user-specific classification of retrieved Web contents into personalized topic hierarchies as well as automatic refinements of such taxonomies using unsupervised machine learning methods (e.g. clustering). Our approach is to combine models from multiple peers and to construct the advanced decision model that takes the generalization performance of multiple ‘local' peer models into account. In addition, meta algorithms can be applied in a restrictive manner, i.e. by leaving out some ‘uncertain' documents. The results of our systematic evaluation show the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

Knowledge and information spanning multiple information sources, multiple media, multiple versions and multiple communities challenge the capabilities of existing knowledge and information management infrastructures by far — primarily in terms of intellectually exploiting the stored knowledge and information. In this paper we present some semantic web technologies of the EU integrated project X-Media that build bridges between the various information sources, the different media, the stations of knowledge management and the different communities. Core to this endeavour is the combination of information extraction with formal ontologies as well as with semantically lightweight folksonomies. [ABSTRACT FROM AUTHOR]

Collaborative data/knowledge management methods aim to achieve improved result quality through combination or merging of results and models obtained from multiple users and sites. Typical application scenarios in the domain of Web information systems include collaborative methods and meta methods for information acquisition (e.g. collaborative Web crawling and search, tagging), organization of document collections (e.g. collaborative classification and clustering), and knowledge sharing (e.g. alignment and sharing of personal ontologies). These areas have received increasing attention in the Web Information Systems community; however, connections and relationships between them have largely been neglected. The workshop on Collaborative Knowledge Management for Web Information Systems (WE.Know) aims at closing this gap by bringing together researchers and practitioners dealing with collaborative methods in distinct contexts, and discovering synergies between their research fields. [ABSTRACT FROM AUTHOR]