RDF generator that produces Linked Data that bear similar characteristics with real datasets, presented at the 1st International Workshop on Benchmarking Linked Data (BLINK). (HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)

1. An RDF Dataset Generator
for the Social Network
Benchmark with
Real-World Coherence
Mirko Spasic, Milos Jovanovik, Arnau Prat-Pérez
Workshop on Benchmarking Linked Data (BLINK) 2016
at the International Semantic Web Conference (ISWC) 2016
18 October 2016, Kobe, Japan

2. Introduction
● Real-world vs Synthetic RDF datasets
● Difference in coherence - a lower degree of structuredness in synthetic
RDF datasets
● Modification of the existing RDF data generator for the Social Network
Benchmark (SNB)
● Virtuoso procedure for coherence measurement of RDF datasets

3. Data Generator for the Social Network
Benchmark
● Existing synthetic RDF data generator for SNB
○ Persons with friendship relations, posts, comments, likes, etc.
○ Models an online social network
● Reproduces the structural characteristics of a real online social network:
○ Attribute correlation
○ Degree distributions
○ Structure-Attribute correlations
○ Spiky activity volume

4. Dataset Coherence
● Composite metric - coherence of a dataset
● Weight coefficient (WT) - higher impact to types with more instances and
properties
● Coverage of type (CV) - depends on whether the instances of the type T set
a value for all its properties

5. Measuring RDF Dataset Coherence in
Virtuoso
● Proposed workflow to compute the coherence of a dataset in a few steps:
○ Assembling all triples into a single file
○ Data cleaning and normalization
○ Generating several temporary files
○ Sorting files in different orders
○ Computing coherence by making a single pass of the sorted file
● Disadvantages: memory requirements, time, temporary space, …
● Virtuoso stored procedure
○ System will take care of the efficacy and data compression ⟹ simpler and much more
space- and time-efficient procedure
○ Available on GitHub

6. A Realistic RDF Dataset Generator for the
SNB
● Most dominant types from the SNB dataset - Post and Comment
● Removal of a high number of triplets related to a specific property
○ isLocatedIn
● Introducing new properties
○ gifFile
○ mentions
○ visibility
○ link
● Available on GitHub

7. Measurements
Original SNB Datasets Modified SNB Datasets

8. Conclusion and Future Work
● Done: A modified RDF Datagen for SNB, with a coherence of ~0.6
○ Used general characteristics of Twitter, Facebook, Instagram, etc., to introduce
modifications to the RDF Datagen
● Done: A set of procedures in Virtuoso for coherence measurement
● Plan: Reduce coverage of other types, apart from Posts and Comments
● Plan: Address the correlation in the newly added parts

9. Q&A
Questions?

10. This work was supported by grands from the EU H2020 Framework Programme
provided for the project HOBBIT (GA no. 688227).