Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA

1. Exploring Semantic Web Data
and particularly Linked Data
Roberto García
AIC Seminar Series
SRI International, Menlo Park, August 14th 2012
Human-Computer Interaction
Universitat de Lleida
and Data Integration
Spain
Research Group

2. Who
• Associate Professor, Universitat de Lleida, Spain
• Visiting Associate Professor, Standford University
– Stanford HCI Group
• +12 years Semantic Web research
– 1999 MSc Thesis: Knowledge Management using
RDF plus reasoning (SiLRI)
– 2006 PhD Thesis: A Semantic Web approach to DRM
– 2006- Copyright Ontology
– 2007- Lleida HCI Group, Semantic Web User
Interfaces

3. What is Open Data?
“Open data is data that can be freely used, reused
and redistributed by anyone - subject only, at most,
to the requirement to attribute and sharealike”
Open Knowledge Foundation
• Make your data OPEN
– Available online with open license
• For instance Creative Commons CC-BY
– No more than reproduction cost
– No matter format

4. Open Data Worldwide
• 169 initiatives Rate:
– City (40), Country, Region or State (125),
Supranational (4)
http://datos.fundacionctic.org/sandbox/catalog/faceted/

5. Welcome to Data.CA.Gov

6. Open Data Formats
• However, encourage formats that facilitate
reuse and interoperability
– Tim Berners-Lee 5 stars classification
http://5stardata.info

7. ★ Open Data
• Make data available on the Web under an
open license
– Data licenses:
• Public Domain Dedication and License (PDDL), Open Data
Commons Attribution License (ODC-by) or Creative Commons
Public Domain Dedication (CC0)
• Whatever format
– Example: PDF
• But… data is locked-up in a document
– Hard to get data out, custom scrapers
http://5stardata.info

8. ★★ Open Data
• Make it available as structured data
– Example: Excel instead of image scan of a table
• But… data still locked-up
– You depend on proprietary software
http://5stardata.info

9. ★★★ Open Data
• Use non-proprietary formats
– Example: CSV instead of Excel
"Temperature forecast for Galway",
"Day","Lowest Temperature (C)"
"Saturday, 13 November 2010",2
"Sunday, 14 November 2010",4
"Monday, 15 November 2010",7
• But… data on the Web and not data in the Web
– What does “Galway” mean? Is it a temperature?
What is the unit? Local time?...
http://5stardata.info

10. Galway (disambiguation)
• Places
– Ireland
• Galway
• County Galway
• Galway Bay
– Sri Lanka
• Galway's Land National Park
– United States
• Galway (town), New York
• Galway (village), New York
• Things
– Galway (sheep), a breed of sheep that originated in Galway, Ireland
– Galway harp, a type of harp
– Galway Hooker, a type of sailing boat
– Galway or Claddagh Ring, a type of wedding ring made in Galway
• …

11. ★★★★ Open Data
• Use URIs to identify things,
so that people can point at your stuff
– Example: RDF1 (but also Atom, OData, JSON-LD,…)
@prefix meteo: <http://purl.org/ns/meteo#> .
@prefix galweather: <http://5stardata.info/galweather#> . Vocabularies
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> . Ontologies
<http://example.org/Galway> meteo:forecast
[ meteo:predicted "2010-11-13T12:00:00Z"^^xsd:dateTime ;
meteo:temperature [ meteo:celsius "2"^^xsd:decimal ] ] .
• But… what if we (humans or computers) don’t
know what http://example.org/Galway means?
1
Resource Description Framework, http://www.w3.org/RDF/

12. ★★★★★ Linked Open Data
• Link your data to other data to provide context
(semantics, meaning)
– Example: http://dbpedia.org/resource/Galway HTTP GET
@prefix dbpedia: <http://dbpedia.org/resource/> .
...
dbpedia:Galway a <http://dbpedia.org/ontology/Place>, <http://dbpedia.org/ontology/PopulatedPlace>,
<http://dbpedia.org/ontology/Settlement>;
rdfs:label "Galway"@en;
dbp:populationBlank "Galwegian, Tribesman"@en;
dbp:populationTotal "75529"^^xsd:int;
dbp:populationUrban "76778"^^xsd:int;
dcterms:subject <http://dbpedia.org/resource/Category:Cities_in_the_Republic_of_Ireland>,
<http://dbpedia.org/resource/Category:County_towns_in_the_Republic_of_Ireland>,
<http://dbpedia.org/resource/Category:Port_cities_and_towns_in_the_Republic_of_Ireland>,
<http://dbpedia.org/resource/Category:University_towns>;
rdfs:comment "Galway or City of Galway (Cathair na Gaillimhe) is a city on the west coast of
Ireland. It is located on the River Corrib between Lough Corrib and Galway Bay and is surrounded by
County Galway. It is the third largest city within the state, though if the wider urban area is
included then it falls into fourth place behind Limerick. The population of Galway city at the 2011
census was 75,529, rising to 76,778 across the entire urban area."@en;
geo:lat 53.2719;
geo:long -9.04889;
foaf:homepage <http://www.galwaycity.ie> .
… and also dbp:PopulationTotal, dct:subject,…

13. Network Effect
~31 billion
statements
http://linkeddata.org

14. Fine for computers… but people?
C. Warren
(blogger)
I’m writing
about “Films I
Like”.
Can I reuse
LinkedMDB?
M. Harper
(developer)
I’m developing
a bird watching
application.
Can I reuse
DBPedia?
http://linkeddata.org

15. User Testing
• Users typical questions:
– Where do I start?
– Where do I go now?
– What is this data about?
– How do I find this?
– …
• What do Linked Data user interfaces offer?

16. DBPedia Scenario
• Linked Data version of Wikipedia
– 3.5 million things described
• Ontology: 257 classes y 1276 properties

17. Target Technical Users
• DBPedia main page

18. Semantic Query Languages
• SPARQL:
– select distinct(?c) (count(?i) as ?n)
where {?i a ?c} order by desc(?n)
c n
http://www.w3.org/2002/07/owl#Thing 1668503
http://www.w3.org/2004/02/skos/core#Concept 632607
http://www.opengis.net/gml/_Feature 571764
http://dbpedia.org/ontology/Place 462349
http://dbpedia.org/ontology/Person 363751
http://dbpedia.org/ontology/Work 355100
http://dbpedia.org/ontology/PopulatedPlace 340443
http://xmlns.com/foaf/0.1/Person 296595

19. Text Search
• What to type? A URI? A URI label?
• How to take advantage from semantics?

20. Semantic Query UIs
• iSPARQL
http://dbpedia.org/isparql/

21. Proposal
Ontologies and dataset structure
Automatic UI Generation Information
Architecture
Components
[Morville]
Overview Menus, Sitemaps,…
Interaction
Patterns for Zoom & Filter Facets
Data Analysis
[Shneiderman]
Details Lists, Maps, Timelines…

22. IA Components. Menus
– From dataset ontologies and thesaurus
• For each class/topic
– URI, label, # instances/uses, subclasses/subtopics
– Flatten to desired # entries and subentries
• When there is room, entries or subentries,
divide class/topic with the most instances
• When too many, group that with the fewest
– “Other” is the generic group

23. IA Components. Menus
7 menus with 10 submenus
Automatic
Generation

24. DEMO
http://rhizomik.net/dbpedia/
IA Components. Menus
Provide DBPedia overview…
…but what about 12.334 birds?

25. IA Components. Facets
• Pre-computed list of facets / class or topic
– Ontologies or thesaurus + instance data
– Facet metrics:
• frequency, #values, most common value
cardinality…
• DBPedia Birds class:
– 226 properties
• dbo:kingdom, 100%, 3 values,
6846 (Animalia),…

26. DEMO
http://rhizomik.net/dbpedia/
Scenario DBPedia

27. DEMO
http://rhizomik.net/linkedmdb
Scenario LinkedMDB

28. Testing LinkedMDB
• Evaluation with lay users as part of RITE1
development process
– Iteration test with 6 users
– LinkedMDB (Linked Data version of iMDb)
User Task:
“Find three films where
Woody Allen is director and
also actor”.
1
Rapid Iterative Testing and Evaluation

30. Evaluation Results
• Seemed easy but…
no user completed task without help
• Really, just 1 issue:
– Users started from “Actor” instead than from
“Film”, and got lost from there
• User interaction is too constrained by
underlying “explicit” data structure
• Lack of context while browsing graph

31. New Features
• Facets for all inverse properties
(explicit or implicit)
– Actor  actor – Film:
• Actor has facet “is actor of Film”
• Breadcrumbs show “query” built so far
– Click Film, then for facet “Actor”
search “Woody Allen”:
• “Showing Film has actor
where actor name is Woody Allen”

32. New Features
• What about getting from Actors to Films to
restrict by director?
• Add Actor facet “directed by”?
– DANGER: facets explosion
• Director  Film  Country  Continent
Director facet:
“continents of countries where films directed”!

33. New Features
• Pivoting: switch from faceted view to
related faceted view (keeping filters)
– E.g.: from Actors facets move to Films facets
through “is Actor of Film” facet
• For each class facet also compute:
– Most specific class for target instances
• Actor “is Actor of” Film and TV Episode
 Audiovisual Work
– Pivot that facet to get:
• Faceted view for target class… + filters so far

34. DEMO
http://rhizomik.net/linkedmdb/

39. Next Round Evaluation
• Semantic Web Exploration Tools
Quality in Use Model:
– Task success, Task time, Satisfaction,…
– UI Component Efficiency, Task Flexibility, Layout
Flexibility,…
• Task: “Films Woody Allen director and actor”
– Task time:
Pre-pivot Pivot Reduction
Minimum 1.05 0.89 15%
Maximum 5.23 2.23 57%
Mean 2.41 1.69 30%
St. Dev. 1.49 0.57 62%

40. Summary
• Menus
– Dataset classes (topics) overview
• Facets
– Filter class using properties and values
• Pivoting
– Switch faceted views, carrying filters

41. DEMO
http://rhizomik.net/linkedmdb/
Conclusions
• Users build queries without SPARQL or
dataset structure knowledge
• Example:
– Who has directed more films in Oceania?
SELECT DISTINCT ?r1 WHERE {
?r1 a movie:Director .
?r2 movie:director ?r1 .
?r2 a movie:Film.
?r2 movie:country ?r3 .
?r3 movie:country_continent ?r3var0
FILTER(str(?r3var0)="Oceania") }

42. Work in Progress
• Interaction design
– Explore the best way to make pivoting, and un-
pivoting, evident for users
– Improve “breadcrumbs”
• Specialized facets:
– Range dependent: histogram for numbers,
calendar for dates,…

43. Work in Progress
Integrate
RDF2SVG

44. Work in Progress
• Object-Action interaction paradigm
– Objet properties determine actions
– Actions: plugable Semantic Web Services
lat, long, point…
time, date, start, end…

45. DEMO
http://lodvisualization.appspot.com
Work in Progress
• Other IA components: sitemaps

46. DEMO
http://rhizomik.net/apollo/
Work in Progress
• Interactively select data and configure
visualizations

47. Data Quality

48. Assisted Edition (and Trust)
WebID
http://www.w3.org/2008/09/msnws/papers/foaf+ssl.html

49. Thanks for your attention
Roberto García
http://rhizomik.net/~roberto
roberto.garcia@udl.cat
Human-Computer Interaction
Universitat de Lleida
and Data Integration
Spain
Research Group