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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Semantics, Web and Mining 2006   book URL 
    BibTeX:
    @book{Semantic2006Ackermann,,
      title = {Semantics, Web and Mining},
      publisher = {Springer},
      year = {2006},
      url = {http://dx.doi.org/10.1007/11908678}
    }
    
    Proceedings of the 2nd Workshop on Semantic Network Analysis 2006   proceedings URL 
    BibTeX:
    @proceedings{alani2006proceedings,,
      title = {Proceedings of the 2nd Workshop on Semantic Network Analysis},
      year = {2006},
      url = {http://www.kde.cs.uni-kassel.de/ws/sna2006/}
    }
    
    Hoser, B., Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. Semantic Network Analysis of Ontologies 2006
    Vol. 4011The Semantic Web: Research and Applications, pp. 514-529 
    inproceedings URL 
    Abstract: A key argument for modeling knowledge in ontologies is the easy
    -use and re-engineering of the knowledge. However, beside
    nsistency checking, current ontology engineering tools provide
    ly basic functionalities for analyzing ontologies. Since
    tologies can be considered as (labeled, directed) graphs, graph
    alysis techniques are a suitable answer for this need. Graph
    alysis has been performed by sociologists for over 60 years, and
    sulted in the vivid research area of Social Network Analysis
    NA). While social network structures in general currently receive
    gh attention in the Semantic Web community, there are only very
    w SNA applications up to now, and virtually none for analyzing the
    ructure of ontologies.

    e illustrate in this paper the benefits of applying SNA to
    tologies and the Semantic Web, and discuss which research topics
    ise on the edge between the two areas. In particular, we discuss
    w different notions of centrality describe the core content and
    ructure of an ontology. From the rather simple notion of degree
    ntrality over betweenness centrality to the more complex
    genvector centrality based on Hermitian matrices, we illustrate
    e insights these measures provide on two ontologies, which are
    fferent in purpose, scope, and size.

    BibTeX:
    @inproceedings{hoser2006semantic,
      author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
      title = {Semantic Network Analysis of Ontologies},
      booktitle = {The Semantic Web: Research and Applications},
      publisher = {Springer},
      year = {2006},
      volume = {4011},
      pages = {514-529},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf}
    }
    
    Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Kollaboratives Wissensmanagement 2006 Semantic Web - Wege zur vernetzten Wissensgesellschaft, pp. 273-290  inbook URL 
    Abstract: Wissensmanagement in zentralisierten Wissensbasen erfordert
    nen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
    mer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
    er zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
    ese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
    nutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
    dere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
    ssensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
    n Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.
    BibTeX:
    @inbook{schmitz2006kollaboratives,
      author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
      title = {Kollaboratives Wissensmanagement},
      booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft},
      publisher = {Springer},
      year = {2006},
      pages = {273-290},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf}
    }
    
    Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Mining Association Rules in Folksonomies 2006 Data Science and Classification. Proceedings of the 10th IFCS Conf., pp. 261-270  inproceedings URL 
    Abstract: Social bookmark tools are rapidly emerging on the Web. In such
    stems users are setting up lightweight conceptual structures
    lled folksonomies. These systems provide currently relatively few
    ructure. We discuss in this paper, how association rule mining
    n be adopted to analyze and structure folksonomies, and how the results can be used
    r ontology learning and supporting emergent semantics. We
    monstrate our approach on a large scale dataset stemming from an
    line system.
    BibTeX:
    @inproceedings{schmitz2006mining,
      author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
      title = {Mining Association Rules in Folksonomies},
      booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
      publisher = {Springer},
      year = {2006},
      pages = {261--270},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf}
    }
    
    Stumme, G., Hotho, A. & Berendt, B. Semantic Web Mining - State of the Art and Future Directions 2006 Journal of Web Semantics
    Vol. 4(2), pp. 124-143 
    article URL 
    Abstract: SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
    is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
    proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
    chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
    b itself.
    e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
    e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
    ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
    b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
    ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
    rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
    rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
    ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
    t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
    tegration could be profitable.
    BibTeX:
    @article{jws2006Semantic,
      author = {Stumme, Gerd and Hotho, Andreas and Berendt, Bettina},
      title = {Semantic Web Mining - State of the Art and Future Directions},
      journal = {Journal of Web Semantics},
      publisher = {Elsevier},
      year = {2006},
      volume = {4},
      number = {2},
      pages = {124-143},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/stumme2006semantic.pdf}
    }
    

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