Semantic Search

Wouter Beek (

After 15 years semantic data cannot be…

  1. found
  2. read
  3. queried
  4. reasoned over

Many PhD students' worse nightmare.

Let's focus on problem 1 today:

Most semantic data cannot be found

  • There is no go-to Semantic Search Engine
  • There are catalogues (CKAN, LOV, VoID-store, LOD Cloud) that contains curated links to data locations on the web.
The SotA in Semantic Web search is comparable to the 1995 Yahoo! index.


But we can travel the graph, right?

“Follow your nose”

Fixing “Follow your nose”

“Follow your nose” on the SW

“Follow your nose” on LOD Laundromat

[6] Semantic Search

LOD Search

2nd place, Linked Data Challenge, 2016. F. Ilievski & W. Beek & M. Van Erp & L. Rietveld & S. Schlobach. 2016. “LOTUS: Adaptive Text Search for Big Linked Data”, ESWC.

Semantic Search Engine

Contextual semantics for owl:sameAs

W. Beek & S. Schlobach & F. Van Harmelen. 2016. “A Contextualised Semantics for owl:sameAs”, International Semantic Web Conference, p. 405--419.

If you do not know SPARQL then you cannot tap into today's SW.

Mike Dean & Jack Margerison

Fixing the NatLang interface

  • Efficient computation through canonicity
  • Data enrichment by improved instance matching
  • User eXperience: language preference, “value labeling”
  • Improve NLP tasks with background knowledge
Ilievski & Beek & Van Erp & Rietveld & Schlobach. 2016. “TODO” European Semantic Web Conference Beek & Ilievski & Debattista & Schlobach “Literally Better: Analyzing and Improving the Quality of Literals” Semantic Web Journal 2016 (under submission)




Natural language entry point to LOD Laundromat:

4.33 billion NatLang literals
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.


Language tags

Thank you for your attention!