WAI

January 18th, 2016

Slides at http://wouterbeek.github.io

LOD Laundromat

The Clean Linked Data platform


  • Decentralized → centralized
  • SPARQL → LDF

Semantic Web Layer Cake

LOD Laundromat Layer Cake

[IEEE Internet Computing, tbp]

“Follow your nose” on the SW

“Follow your nose” on LOD Laundromat

“A new development we observe is the building of a custom API on top of a SPARQL endpoint. A custom API ensures that only a small number of SPARQL patterns can be queried for. This significantly simplifies endpoint optimization. We do not think this is a good development. The deficiencies of the existing deployment paradigm should not result in the altogether abandonment of the idea of a machine-processable Web. Doing away with dereferenceability and SPARQL as the only or even main ways of disseminating Linked Open Data may be necessary to save the larger goal of creating a machine-processable Web.”

[IEEE Internet Computing, tbp]

Healthy markets exhibit allocative efficiency

The price a consumer pays equals the marginal cost of production.

Since a client pays nothing and the marginal cost of production is relatively high, the SPARQL paradigm is inherently far removed from allocative efficiency.

[IEEE Internet Computing, tbp]

Datasets used in ISWC 2014 research track papers

17 datasets are used in total

1-6 datasets per article

2 datasets per article on average










L. Rietveld & W. Beek & S. Schlobach “LOD Lab: Experiments at LOD Scale” International Semantic Web Conference, 2015.

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.

LOTUS

lotus.lodlaundromat.org

Why literals?

  • Concise notation for infinite value spaces.
  • Encoding of linguistic/text-based content.



Why high-quality literals?

  • Efficient computation through canonicity
  • Data enrichment by improved instance matching
  • User eXperience: language preference, “value labeling”
  • Improve NLP tasks with background knowledge



W. Beek & F. Ilievski & J. Debattista & S. Schlobach Literally Better: Analyzing and Improving the Quality of Literals, under submission


W. Beek & F. Ilievski & J. Debattista & S. Schlobach Literally Better: Analyzing and Improving the Quality of Literals, under submission


W. Beek & F. Ilievski & J. Debattista & S. Schlobach Literally Better: Analyzing and Improving the Quality of Literals, under submission

“incremental, and lacking in innovation”

1.3

Triply

Linked Business Data Deployed