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Link Discovery over geo RDF data: content similarity approach
Geo RDF knowledge graph is a very important part of the linked data. Linking datasets contain Geospatial data is very interesting
topic in academia and industry. Accordingly, many approachs have been introduced to address the
problem of link discovery over such data taking in account the scalability and the accuracy as two central
factors when such a framework to be implemented. In this work, we plan to implement a Link Discovery
(LD) over geo RDF dataset then we compare it with the current state of the art (e.g. RADON, Sherif et al).
The work will be as follows:
- literature review on LD over geo RDF, topological relations such as 9IM-ED.
- Implementing content measure in JAVA based on the paper ( GODOY et al ).
- Evaluate the approach on real datasets such as NUTs.
- Compare the results with RADON algorithms in term of the scalability( run time) and the accuracy (F- measure).
- Publishing the results in a scientific conference in case of a promising results.
- Java programming (good practical experience)
- Math knowledge ( Algebra)
- RDF, Semantic, Topological geomatric relations
- Godoy et al: Defining and Comparing Content Measures of Topological Relations
- Sherif et al: Radon— Rapid Discovery of Topological Relations