Knowledge Discovery Group

Inverse Link Traversal on Linked Open Data

Description

Query execution using link-traversal is a promising approach for retrieving and accessing data on the web [1]. However, this approach finds its limitation when it comes to query patterns such as ?s rdf:type ex:Employee, where one does not know the subject URI. Such queries are quite useful for different application needs.

In this thesis, we aim to conduct an empirical analysis on the use of such patterns in SPARQL query logs. We develop and empirically evaluate different solution approaches to extend the current Linked Open Data principles with the ability for inverse link traversal. We discuss the advantages and disadvantages of the different approaches.

In more detail, the work should cover:
- Development of traversal strategies for Linked Open Data
- Incorporation of the strategies in a prototype
- Evaluation on a suitable datacorpus

This work will be conducted in collaboration with Stefan Scheglmann from the University of Koblenz-Landau.

Requirements

- Good programming skills
- Knowledge of Semantic Web techniques are of advantage
- Management of large data sets will be necessary

Literature
[1] Stefan Scheglmann and Ansgar Scherp: Will Linked Data Benefit from Inverse Link Traversal?, Linked Data on the Web (LDOW2014), Seoul, Korea, April, 2014.
http://events.linkeddata.org/ldow2014/papers/ldow2014_paper_08.pdf

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