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Semantic Data License Modeling, Storage and Querying (Master)

Type: 
Master
Supervisor: 

The thesis work will contribute to Data Licenses Clearance Center - DALICC project, and specifically address the semantic aspects of data license modeling, storage, querying and reuse.

About DALICC project: The creation of derivative data works, i.e. for purposes like content creation, service delivery or process automation, is often accompanied by legal uncertainty about usage rights and high costs in the clearance of licensing issues. The DALICC project aims to develop a software framework that supports the automated clearance of rights issues in the creation of derivative data works. In essence, DALICC helps to determine which information can be shared with whom to what extent under which conditions, thus lowering the costs of rights clearance and stimulating the data economy.

For License Modelling, the ontology representing the license data model is to be defined and the system constraints will be modelled using semantic rules in the state of the art manner. This will require an initial research in the rule languages most suitable for the types of use cases and the respective data repositories, as well as linked data background.

The linked data empowered data repository for storing the structured semantic license data is to be set up. The data access will then be provided via appropriate interfaces using e.g. REST/Web Service access, SPARQL endpoint (for semantic data), etc. Appropriate provisions will be made to enable secure access to data by third party agents. End user services will also be able to query data through their mobile and tablet devices. The repository will serve as a frontal point to anybody who wants to address checking of the licenses’ specifics and their compatibility, using external reasoning engines or license design tools.

 

References

Ermilov, I., & Pellegrini, T. (2015, September). Data licensing on the
cloud: empirical insights and implications for linked data. In Proceedings of the 11th International Conference on Semantic Systems (pp. 153-156). ACM.

 

Steyskal, S., & Polleres, A. (2014, September). Defining expressive access policies for linked data using the ODRL ontology 2.0. In Proceedings of the 10th International Conference on Semantic Systems (pp. 20-23). ACM.

 

Zhdanova, A. V., Zeiß, J., Dantcheva, A., Gabner, R., & Bessler, S. 
(2009). A semantic policy management environment for end-users and its empirical study. In Networked Knowledge-Networked Media (pp. 249-267). Springer Berlin Heidelberg.

 

Datahub, data repository: https://datahub.io

LOV, Linked Open Vocabularies: http://lov.okfn.org