Semantic Web - 6. Linked Open Data - WeST – Uni … Web 6. Linked Open Data Gerd Gr oner, Matthias Thimm fgroener,[email protected] Institute for Web Science and Technologies

Semantic Web6. Linked Open Data

Gerd Groner, Matthias Thimm

{groener,thimm}@uni-koblenz.de

Institute for Web Science and Technologies (WeST)University of Koblenz-Landau

July 12, 2013

Gerd Groner, Matthias Thimm Semantic Web 1 / 37

RDF, RDFS, OWL vocabularies

I RDFI rdf:StatementI rdf:typeI rdf:subject, rdf:predicate, rdf:objectI . . .

I RDFSI rdfs:ResourceI rdfs:ClassI rdfs:subClassOfI . . .

I OWLI owl:disjointOfI owl:sameAsI owl:allValuesFromI . . .

What is missing?






What is missing?






What is missing?






What is missing?


Linked Open Data

→ everyone has to use it! Properly!


Linked Open Data

I RDF, RDFS, OWL can be used to describe a local isolateddata set

I linking resources to other data sets openly enables linked opendata


Linked Open Data

I RDF encourages to use URIs to denote things

I Linked Open Data demands it

Tim Berners-Lee:“Don’t say “colour” say <http://example.com/2002/std6#col>”


Linked Open Data





Linked Open Data





Outline

1 Linked Open Data Principles

2 Some examples

3 Applications

4 Summary and Exercises


Outline


2 Some examples

3 Applications



Linked Open Data Principles

I Linked Open Data PrinciplesI use URIs as names for thingsI use HTTP URIs so those names can be dereferencedI return useful information upon lookup of those URIs (e.g.

RDF)I include links by using URIs that dereference to remote

documents

I Linked Open Data SchemeI Publish under an open licenseI Publish structured dataI Use non-proprietary formatsI Use URIs to identify thingsI Link you data to other data


Linked Open Data Principles

I Linked Open Data PrinciplesI use URIs as names for thingsI use HTTP URIs so those names can be dereferencedI return useful information upon lookup of those URIs (e.g.

RDF)I include links by using URIs that dereference to remote

documents



Five stars of Linked Open Data conformance

5stardata.info


5stardata.info

Publish under an open license

As a consumer

I You can store it locally (on your hard drive or on an USBstick).

I You can enter the data into any other system.

I You can change the data as you wish.

I You can share the data with anyone you like.

As a publisher

I It’s simple to publish.

I You do not have explain repeatedly to others that they canuse your data.

5stardata.info


5stardata.info

Publish structured data

As a consumer

I You can directly process it with proprietary software toaggregate it, perform calculations, visualise it, etc.

I You can export it into another (structured) format.

As a publisher

I It’s still simple to publish.

5stardata.info


5stardata.info

Use non-propriety formats

As a consumer

I You can manipulate the data in any way you like, withoutbeing confined by the capabilities of any particular software.

As a publisher

I You might need converters or plug-ins to export the data fromthe proprietary format.

I It’s still rather simple to publish

5stardata.info


5stardata.info

Use URIs to identify things

As a consumer

I You can link to it from any other place (on the Web orlocally).

I You may be able to reuse existing tools and libraries, even ifthey only understand parts of the pattern the publisher used.

I Understanding the structure of an RDF Graph of data can bemore effort

I You can combine the data safely with other data.

As a publisher

I Other data publishers can now link into your data

I You typically invest some time slicing and dicing your data.

I You’ll need to assign URIs to data items and think about howto represent the data.

5stardata.info


5stardata.info

Link your data to other data

As a consumerI You can discover more data while consuming the data.I You can directly learn about the data schema.I You now have to deal with broken data links.I Presenting data from an arbitrary link as fact is as risky as

letting people include content from any website in your pages.Caution, trust and common sense are all still necessary.

As a publisherI You make your data discoverable.I You increase the value of your data.I You own organisation will gain the same benefits from the

links as the consumers.I You’ll need to invest resources to link your data to other data

on the Web.I You may need to repair broken or incorrect links.

5stardata.info


5stardata.info

Outline


2 Some examples

3 Applications



DBpedia 1/2

I “DBpedia is a crowd-sourced community effort toextract structured information from Wikipedia andmake this information available on the Web.”

I “Semantification” of Wikipedia:I 764,000 personsI 573,000 placesI 333,000 creative worksI 192,000 organizationsI 202,000 speciesI 5,500 diseases.


DBpedia 2/2

Excerpt from the DBpedia ontology:

I owl:ThingI dbpedia:Agent

I dbpedia:FamilyI dbpedia:OrganisationI dbpedia:Person

I dbpedia:EventI dbpedia:ConventionI dbpedia:FilmFestivalI dbpedia:SportsEvent

I dbpedia:LanguageI dbpedia:NameI dbpedia:Place

I dbpedia:MonumentI dbpedia:PopulatedPlaceI dbpedia:WineRegion


FOAF

I FOAF=Friend of a FriendI Vocabulary for describing people, links between them, things

they create and do


Dublin Core

I Vocabulary for describing the creation and publishing ofgeneral works

I Namespace:xmlns:dc="http://purl.org/dc/elements/1.1"

I ExamplesI dc:creatorI dc:titleI dc:rightsI dc:publisherI dc:dateI dc:descriptionI dc:sourceI dc:language


Dublin Core





Dublin Core





Dublin Core





Dublin Core





Dublin Core





Dublin Core





Dublin Core





Dublin Core





Outline


2 Some examples

3 Applications



Applications

I What can I do with this?


Use case example: SchemEx

courtesy of Ansgar Scherp


Use case example: SchemEx (cont’d)



Naive Approach:

I Download the entire LOD cloud

I Put it into a (really) large triple store

I Process the data and extract schema

I Provide lookup

Drawbacks:

I Big machinery

I Late in processing the data

I High effort to scale with LOD cloud

Can we do it smarter?



Naive Approach:




I Provide lookup

Drawbacks:

I Big machinery






Naive Approach:




I Provide lookup

Drawbacks:

I Big machinery






Idea:I Schema-level index

I Define families of graph patternsI Assign instances to graph patternsI Map graph patterns to context (source URI)

I ConstructionI Stream-based for scalabilityI Little loss of accuracy

I NoteI Index defined over instancesI But stores the context






























Outline


2 Some examples

3 Applications



Summary

I Linked Open Data as the application area of RDF, RDFS,OWL


I Examples: DBpedia, FOAF, Dublin Core

I Applications: SchemEx


Pointers to further reading

I 5stardata.info The five stars of Linked Open DataConformance

I A. Hogan, J. Umbrich, A. Hart, R. Cyganiak, A. Polleres, S.Decker. An empirical survey of Linked Data conformance. InJournal of Web Semantics, 14:14–44, 2012.

I M. Konrath, T. Gottron, S. Staab and A. Scherp: SchemEX ?Efficient Construction of a Data Catalogue by Stream-basedIndexing of Linked Data, Journal of Web Semantics: Science,Services and Agents on the World Wide Web, Special Issue onSemantic Web Callenge 2011, 2012.


5stardata.info

Exercises

No exercises today

Have a nice weekend!


Documents

Semantic Web - 6. Linked Open Data - WeST – Uni … Web 6. Linked Open Data Gerd Gr oner, Matthias Thimm fgroener,[email protected] Institute for Web Science and Technologies