The success of Open Data initiatives has increased the amount of data available on the Web. Unfortunately, most of this data is only available in raw tabular form, what makes analysis and reuse quite difficult for non-experts. Linked Data principles allow for a more sophisticated approach by making explicit both the structure and semantics of the data. However, from the end-user viewpoint, they continue to be monolithic files completely opaque or difficult to explore by making tedious semantic queries. Our objective is to facilitate the user to grasp what kind of entities are in the dataset, how they are interrelated, which are their main properties and values, etc. Rhizomer is a tool for data publishing whose interface provides a set of components borrowed from Information Architecture (IA) that facilitate awareness of the dataset at hand. It automatically generates navigation menus and facets based on the kinds of things in the dataset and how they are described through metadata properties and values. Moreover, motivated by recent tests with end-users, it also provides the possibility to pivot among the faceted views created for each class of resources in the dataset.

1. Facets and Pivoting for Flexible and
Usable Linked Data Exploration
Josep Maria Brunetti, Rosa Gil, Roberto García
In t e r a c t in g w it h L in k e d D a t a
W o r k s h o p , I L D ’ 12
Crete, Greece, May 28th 2012
Human-Computer Interaction
Universitat de Lleida
and Data Integration
Spain
Research Group

2. Starting Point
• Rhizomer
Semantic Web Data publishing
HTML+RDF “semantic”
a FORMS
SPARQL or LinkedData new edit delete
POST
GET
DEL
PUT
RhizomerApp
Metadata Jena, Virtuoso, OWLIM,…
Store

3. Interacting
• Useful for computers…
but also for lay users?
• User tests:
– Typical questions:
• Where do I start?
• Where do I go now?
• What is this data about?
– What do we offer?
• Text search, type URI, SPARQL query,…
…but they usually don’t answer lay users needs

4. Interacting
• Example: What to do with DBPedia?
– 3.5 million things described
• Ontology: 257 classes y 1276 properties

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

6. IA Components. Menus
– Hierarchical structure for dataset ontologies
• For each class
– URI, label, # instances, subclasses
– Flatten to desired # entries and subentries
• When there is room, divide class with most
instances
• When too many options, group classes with less
instances

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

8. IA Components. Menus
Navigation bar provides overview for DBPedia…
…but what to do with 12.334 birds now?

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

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

12. Evaluation
• 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

13. Proposals
• Facet 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”:
• Display:
“Showing Film has actor where actor name is Woody Allen”

14. Proposals
• What about getting from Actors to Films to
restrict by director?
• Add Actor facet “directed by”?
– DANGER: facets explosion
• Director facet “continents of countries where films
directed”!

15. Proposals
• 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  Work
– Pivot that facet to get:
• Faceted view for target class
• … + filters so far

21. Conclusions
• Menus
– Dataset classes (topics) overview
• Facets
– Per class properties and values, filter
• Pivoting
– Switch faceted views, carry on filters

22. 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") }

23. Future Work
• User evaluation
– Explore the best way to provide pivoting,
and un-pivoting…
• Specialised facets:
– Range dependent: histogram for numbers,
calendar for dates,…
• Other IA components: sitemaps
• …

24. Thanks for your attention
Roberto García
http://rhizomik.net/~roberto/
Human-Computer Interaction
and Data Integration Universitat de Lleida
Research Group