The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for “popular ” sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

Abstract. The Fedora architecture is an extensible framework for the storage, management, and dissemination of complex objects and the relationships among them. Fedora accommodates the aggregation of local and distributed content into digital objects and the association of services with objects. This allows an object to have several accessible representations, some of them dynamically produced. The architecture includes a generic RDF-based relationship model that represents relationships among objects and their components. Queries against these relationships are supported by an RDF triple store. The architecture is implemented as a web service, with all aspects of the complex object architecture and related management functions exposed through REST and SOAP interfaces. The implementation is available as open-source software, providing the foundation for a variety of end-user applications for digital libraries, archives, institutional repositories, and learning object systems. 1

Ecologists spend considerable e#ort integrating heterogeneous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this e#ort by providing data integration and transformation tools, allowing researchers to focus on "real science," that is, discovering new knowledge through analysis and modeling. This paper defines a generic framework for transforming heterogeneous data within scientific workflows. Our approach relies on a formalized ontology, which serves as a simple, unstructured global schema. In the framework, inputs and outputs of services within scientific workflows can have structural types and separate semantic types (expressions of the target ontology). In addition, a registration mapping can be defined to relate input and output structural types to their corresponding semantic types. Using registration mappings, appropriate data transformations can then be generated for each desired service composition. Here, we describe our proposed framework and an initial implementation for services that consume and produce XML data.

Generating hypermedia presentations requires processing constituent material into coherent, unified presentations. One large challenge is creating a generic process for producing hypermedia presentations from the semantics of potentially unfamiliar domains. The resulting presentations must both respect the underlying semantics and appear as coherent, plausible and, if possible, pleasant to the user. Among the related unsolved problems is the inclusion of discourse knowledge in the generation process. One potential approach is generating a discourse structure derived from generic processing of the underlying domain semantics, transforming this to a structured progression and then using this to steer the choice of hypermedia communicative devices used to convey the actual information in the resulting presentation.

this paper we address the Hera design methodology and specifically explain two models: the integration model that covers the di#erent aspects of integration, and the adaptation model that specifies how the generated presentations can be adaptable (e.g. based on device capabilities, user preferences) and adaptive (e.g. based on user browsing history). This detailed description includes an explanation of how the Hera software is constructed. This software provides a set of transformations that allow a WIS to go from integration to presentation generation

Abstract This paper presents a minimalist program for RDF, by showing how one can do without several predicates and keywords of the RDF Schema vocabulary, obtaining a simpler language which preserves the original semantics. This approach is beneficial in at least two directions: (a) To have a simple abstract fragment of RDFS easy to formalize and to reason about, which captures the essence of RDFS; (b) To obtain algorithmic properties of deduction and optimizations that are relevant for particular fragments. Among our results are: the identification of a simple fragment of RDFS; the proof that it encompasses the main features of RDFS; a formal semantics and a deductive system for it; sound and complete deductive systems for their sub-fragments; and an O(n log n) complexity bound for ground entailment in this fragment. 1

The Semantic Web languages RDFS and OWL have been around for some time now. However, the presence of these languages has not brought the breakthrough of the Semantic Web the creators of the languages had hoped for. OWL has a number of problems in the area of interoperability and usability in the context of many practical application scenarios which impede the connection to the Software Engineering and Database communities. In this paper we present OWL Flight, which is loosely based on OWL, but the semantics is grounded in Logic Programming rather than Description Logics, and it borrows the constraint-based modeling style common in databases. This results in di#erent types of modeling primitives and enforces a di#erent style of ontology modeling. We analyze the modeling paradigms of OWL DL and OWL Flight, as well as reasoning tasks supported by both languages. We argue that di#erent applications on the Semantic Web require di#erent styles of modeling and thus both types of languages are required for the Semantic Web.

Wikipedia is the biggest collaboratively created source of encyclopaedic knowledge. Growing beyond the borders of any traditional encyclopaedia, it is facing new problems of knowledge management: The current excessive usage of article lists and categories witnesses the fact that 19th century content organization technologies like inter-article references and indices are no longer su#cient for today's needs.

While the idea that querying mechanisms for complex relationships (otherwise known as Semantic Associations) should be integral to Semantic Web search technologies has recently gained some ground, the issue of how search results will be ranked remains largely unaddressed. Since it is expected that the number of relationships between entities in a knowledge base will be much larger than the number of entities themselves, the likelihood that Semantic Association searches would result in an overwhelming number of results for users is increased, therefore elevating the need for appropriate ranking schemes. Furthermore, it is unlikely that ranking schemes for ranking entities (documents, resources, etc.) may be applied to complex structures such as Semantic Associations. In this paper, we present an approach that ranks results based on how predictable a result might be for users. It is based on a relevance model SemRank, which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches, where users may vary their search modes to effect changes in the ordering of results depending on their need. We also present the infrastructure used in the SSARK system to support the computation of SemRank values for resulting Semantic Associations and their ordering.

Abstract. To integrate office appliances, there are different standards, consisting of data formats and communication protocols. The WWW and Semantic Web standards are already designed for worldwide integration and can be transferred to office integration. We present our vision of the Semantic Desktop – a Semantic Web enhanced desktop environment. Central is the idea of taking know-how from the Semantic Web to tackle personal information management. The architecture is based on a Semantic Web Server running as Desktop service. Existing desktop applications (email client, browser, office applications) are integrated. The semantic glue between them is expressed with ontologies. This architecture will enable us to create tools for information management faster and cheaper. Based on the local Semantic Desktop Servers, teams of knowledge workers can set up peer-to-peer connections. Distributed Organisational Memories can be based on Semantic Desktops. The gnowsis framework is an open source project led by the DFKI that realizes parts of this vision. Gnowsis was used to test our ideas and allow others to experiment. 1