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Bachelor Thesis: Dataset Discovery in the Linking Open Data Cloud using Recommendation Methods
Dataset Discovery in the Linking Open Data Cloud using Recommendation Methods
The number of datasets in the Linking Open Data Cloud  increases rapidly and has reached a total of 295 by September 2011. Various content domains are covered by those datasets, such as Media, Life Sciences or Government, all directly linked to each other building a huge graph of interlinked structured data. Often, there are also several datasets containing similar data related to the same content domain.
However, due to this complexity, finding the right/best/most appropriate datasets from the LOD Cloud  to interlink with a newly created one is very time-consuming and requires manual effort. One possible solution for automatically finding suitable datasets could be the exploitation of recommendation algorithms in order to generate dataset-as-a-whole recommendations that fit to the newly created dataset in terms of interlinking resources.
The main objective of this thesis is to analyze the principles of Linked Data and the basic recommendation methods in order to find solutions for dataset recommendations that fit to a newly created dataset in terms of interlinking. For this purpose, recommendation methods should be developed that calculate similarities between datasets in the LOD Cloud  and generate interlinkable dataset recommendations for an exemplary modeled dataset.
- Analyze the principles of Linked Data and the basic recommendation methods
- Create concepts and solutions for interlinkable dataset recommendations
- Demonstrate the concepts and solutions via an exemplary dataset
- Interest in Linked Data and Recommendation Methods
- Knowledge of Semantic Web and Recommendation Methods might be useful
- Proficiency in object-oriented programming
Supervisor: Prof. Dr. Axel Küpper , Abdulbaki Uzun 
Type: Bachelor Thesis
Duration: 4 months
10587 Berlin, Germany
Phone: +49 30 8353 58811
Fax: +49 30 8353 58409