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Facets and Pivoting for Flexible and Usable Linked Data Exploration
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Facets and Pivoting for Flexible and Usable Linked Data Exploration


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 …

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.

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  • 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 2012Human-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 FacetsData 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. Menus7 menus with 10 submenus Automatic Generation
  • 8. IA Components. MenusNavigation 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
  • 11. 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
  • 12. 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”
  • 13. 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”!
  • 14. 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
  • 15. Conclusions• Menus – Dataset classes (topics) overview• Facets – Per class properties and values, filter• Pivoting – Switch faceted views, carry on filters
  • 16. 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") }
  • 17. 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• …
  • 18. Thanks for your attention Roberto García Interaction and Data Integration Universitat de Lleida Research Group