Advertisement

Media, Politics and the Semantic Web

An Experience Report in Advanced RDF Usage
  • Wouter van Atteveldt
  • Stefan Schlobach
  • Frank van Harmelen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)

Abstract

The media play an important role in the functioning of our society. This role is extensively studied by Communication Scientists, requiring a systematic analysis of media content. The methods developed in this field utilize complex data models and background knowledge. This data is generally represented ad hoc, making it difficult to analyze, combine and share data sets.

In this paper we present our work on formalizing this representation using RDF(S). We discuss the requirements for a good representation, highlighting a number of non-trivial modeling decisions. We conclude with a description of the resulting system and the benefits for a recent investigation of the 2006 Dutch parliamentary campaign. This case study shows concrete improvements for annotating, querying, and analyzing data, but also indicates a number of aspects that were more difficult to model in RDF(S), contributing to the discussion on modeling with and improving RDF(S) and associated tools.

Keywords

Model Check Background Knowledge Multiplex Network Extra Argument Role Membership 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Krippendorff, K.: Content Analysis: An Introduction to Its Methodology, 2nd edn. Sage Publications, Thousand Oaks (2004)Google Scholar
  2. 2.
    Carley, K.: Network text analysis: The network position of concepts. In: Roberts, C. (ed.) Text Analysis for the Social Sciences, pp. 79–100. Lawerence Erlbaum Associates, Mahwah (1997)Google Scholar
  3. 3.
    Kleinnijenhuis, J., Scholten, O., van Atteveldt, W., van Hoof, A., Krouwel, A., Oegema, D., de Ridder, J.A., Ruigrok, N., Takens, J.: Nederland vijfstromenland: De rol van media en stemwijzers bij de verkiezingen van 2006. Bert Bakker, Amsterdam (2006)Google Scholar
  4. 4.
    Van Cuilenburg, J.J., Kleinnijenhuis, J., De Ridder, J.A.: Towards a graph theory of journalistic texts. European Journal of Communication 1, 65–96 (1986)CrossRefGoogle Scholar
  5. 5.
    Wiebe, J.M., Wilson, T., Bruce, R.F., Bell, M., Martin, M.: Learning subjective language. Computational Linguistics 30(3), 277–308 (2004)CrossRefGoogle Scholar
  6. 6.
    Van Atteveldt, W., Kleinnijenhuis, J., Carley, K.: Rcadf: Towards a relational content analysis standard. In: Presented at the International Communication Association (ICA), Dresden (2006)Google Scholar
  7. 7.
    Van Atteveldt, W., Oegema, D., van Zijl, E., Vermeulen, I., Kleinnijenhuis, J.: Extraction of semantic information: New models and old thesauri. In: Proceedings of the RC33 Conference on Social Science Methodology, Amsterdam (2004)Google Scholar
  8. 8.
    Wasserman, S., Faust, K.: Social Network Analysis. CUP, Cambridge (1994)Google Scholar
  9. 9.
    MacGregor, R., Ko, I.-Y.: Representing contextualized data using semantic web tools. In: Practical and Scalable Semantic Web Systems, workshop at second ISWC (2003)Google Scholar
  10. 10.
    Carroll, J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and trust. In: Proceedings of the Fourteenth International World Wide Web Conference (WWW2005), Chiba, Japan, vol. 14, pp. 613–622 (2005)Google Scholar
  11. 11.
    Brickley, D., Guha, R.: Rdf vocabulary description language 1.0: Rdf schema. W3C Recommendation (2004), http://www.w3.org/TR/rdf-schema/
  12. 12.
    Noy, N., Rector, A.: Defining n-ary relations on the semantic web. Working Draft for the W3C Semantic Web best practices group (2005)Google Scholar
  13. 13.
    Dumbill, E.: Tracking provenance of rdf data. Technical report, ISO/IEC (2003)Google Scholar
  14. 14.
    Guha, R., McCool, R., Fikes, R.: Contexts for the semantic web. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 32–46. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Sintek, M., Decker, S.: TRIPLE–A query, inference, and transformation language for the semantic web. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, p. 364. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Masolo, C., Vieu, L., Bottazzi, E., Catenacci, C., Ferrario, R., Gangemi, A., Guarino, N.: Social roles and their descriptions. In: Dubois, D., Welty, C., Williams, M.A. (eds.) Proceedings of the Ninth International Conference on the Principles of Knowledge Representation and Reasoning (KR2004), Whistler, Canada, pp. 267–277 (2004)Google Scholar
  17. 17.
    Steimann, F.: On the representation of roles in object-oriented and conceptual modelling. Data and Knowledge Engineering 35, 83–106 (2000)zbMATHCrossRefGoogle Scholar
  18. 18.
    Sowa, J.F.: Using a lexicon of canonical graphs in a semantic interpreter. In: Evens, M.W. (ed.) Relational models of the lexicon, Cambridge University Press, Cambridge (1988)Google Scholar
  19. 19.
    Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole, Pacific Grove (2000)Google Scholar
  20. 20.
    Guarino, N.: Concepts, attributes and arbitrary relations: Some linguistic and ontological criteria for structuring knowledge bases. Data and Knowledge Engineering 8, 249–261 (1992)CrossRefGoogle Scholar
  21. 21.
    Mika, P., Gangemi, A.: Descriptions of Social Relations. In: Proceedings of the 1st Workshop on Friend of a Friend, Social Networking and the (Semantic) Web (2004), http://www.w3.org/2001/sw/Europe/events/foaf-galway/papers/fp/descriptions_of_social_relations/

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Wouter van Atteveldt
    • 1
  • Stefan Schlobach
    • 1
  • Frank van Harmelen
    • 1
  1. 1.Department of Artificial Intelligence, Free University Amsterdam (The Netherlands), De Boelelaan 1071, 1071 HV Amsterdam 

Personalised recommendations