Linked Open Data Statistics:
Collection and Exploitation

Ivan Ermilov, Michael Martin, Jens Lehmann, Sren Auer

Agenda

Why LODStats?

Architecture

Core Module

Web Interface

RDF DataCube

Why LODStats?

Evaluate RDF datasets

Gathers 32 statistical criteria such as:Number of triples, entities, literals...

Average string length

Vocabularies, classes used

Helps you understand your data

Generates VoID descriptions

Why LODStats?

LODStats can answer the following questions:Which visualization is suitable for my dataset?Does data contain RDF DataCubes?

Does data contain geospatial information?

What is the class hierarchy depth?

Is it suitable for my application?Is it linked to DBpedia?

Is it the largest existing dataset in this domain?

LODStats Architecture

https://github.com/AKSW/LODStats

https://github.com/AKSW/LODStats_WWW

LODStats: Core Module

Python module (tested on Ubuntu 12.04)

Command line interface

Do the actual work

LODStats: Web Interface

http://stats.lod2.eu/

LODStats Features (1)

General LOD cloud statistics

Report of warnings and errors for each dataset

Report on statistical criteria for each individual dataset

Export as VoID and DataCube statistical metadata

Dataset linkage explorer

Search function for datasets, vocabularies, classes, properties, languages, datatypes

LODStats Features (2)

REST interface for the above search functions

Linked Data publication of statistics

SPARQL endpoint to query all extracted statistics

CubeViz installation for facet-based browsing and visualisation of the statistical metadata

LODStats: Class Stats

http://stats.lod2.eu/

LODStats RDF DataCube

https://github.com/AKSW/LODStats_WWW/blob/master/LODStats-Sparqlify/lodstats.sml

LODStats RDF DataCube

Thank you for your attention! Questions?

Ivan Ermiloviermilov@informatik.uni-leipzig.de