LOD Laundromat Tutorial

April 18th, 2016

Wouter Beek & Filip Ilievski

[1] Find IRIs



Mike Dean & Jack Margerison

LOTUS

NatLang entry point to LOD Laundromat

Large-scale: 4.33 billion NatLang literals

Configurable: Filtering based on original language, auto-detected language, subject, predicate (32 retrieval options)


Ilievski & Beek & Van Erp & Rietveld & Schlobach “LOTUS: Adaptive Text Search for Big Linked Data” ESWC 2016

LOTUS

lotus.lodlaundromat.org

lotus.lodlaundromat.org/docs

[2] Find statements

Authority

AAA: Anyone can say anything about anything

AAA⋆: Even though everyone can say anything about anything, very few people are heard

Beek & Rietveld & Schlobach & Van Harmelen “LOD Laundromat: Why the Semantic Web Needs Centralization (Even If We Don't Like It)” IEEE Internet Computing 20 (2) p.78-81, 2016

Frank

Federated Resource Architecture for Networked Knowledge

https://github.com/LOD-Laundromat/Frank

W. Beek & L. Rietveld. “Frank: The LOD Cloud at your Fingertips” Extended Semantic Web Conference: Developers Workshop, 2015.

./frank statements -s http://dbpedia.org/resource/Monkey -g

[3] Find documents

index.lodlaundromat.org

./frank documents --namespace http://www.w3.org/2006/vcard/ns# | ./frank statements


./frank documents --minTriples 880 --maxTriples 900 -d

[4] Scale your LOD evaluation

http://lodlaundromat.org/lodlab


Rietveld & Beek & Schlobach “LOD Lab: Experiments at LOD Scale” International Semantic Web Conference 2015 (Best Paper Award)

./frank statements -p foaf:knows | grep last-fm > last-fm.nt