ShareAlike Your Data: Self-referential Usage Policies for the Semantic Web

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ShareAlike Your Data: Self-referential Usage Policies for the Semantic Web

Markus KrötzschMarkus Krötzsch,  Sebastian SpeiserSebastian Speiser
ShareAlike Your Data: Self-referential Usage Policies for the Semantic Web


Markus Krötzsch, Sebastian Speiser
ShareAlike Your Data: Self-referential Usage Policies for the Semantic Web
Proc. 10th International Semantic Web Conference (ISWC'11), 354-369, October 2011. Springer
  • KurzfassungAbstract
    Numerous forms of policies, licensing terms, and related conditions are associated with Web data and services. A natural goal for facilitating the re-use and re-combination of such content is to model usage policies as part of the data so as to enable their exchange and automated processing. This paper thus proposes a concrete policy modelling language. A particular difficulty are self-referential policies such as Creative Commons ShareAlike, that mandate that derived content is published under some license with the same permissions and requirements. We present a general semantic framework for evaluating such recursive statements, show that it has desirable formal properties, and explain how it can be evaluated using existing tools. We then show that our approach is compatible with both OWL DL and Datalog, and illustrate how one can concretely model self-referential policies in these languages to obtain the desired conclusions.
  • Weitere Informationen unter:Further Information: Link
  • Forschungsgruppe:Research Group: Computational LogicComputational LogicWissensbasierte SystemeKnowledge-Based Systems
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25073-6_23.
@inproceedings{KS2011,
  author    = {Markus Kr{\"{o}}tzsch and Sebastian Speiser},
  title     = {ShareAlike Your Data: Self-referential Usage Policies for the
               Semantic Web},
  booktitle = {Proc. 10th International Semantic Web Conference (ISWC'11)},
  publisher = {Springer},
  year      = {2011},
  month     = {October},
  pages     = {354-369},
  doi       = {10.1007/978-3-642-25073-6_23}
}