Facet Graphs: Complex Semantic Querying Made Easy

ESWC 20107th Extended Semantic Web Conference Heraklion, Greece – June 1-3, 2010 Facet Graphs: Complex Semantic Querying Made Easy Philipp Heim1, Thomas Ertl1 and Jürgen Ziegler2 1 Visualization and Interactive Systems Group (VIS), University of Stuttgart, Germany 2 Interactive Systems and Interaction Design, University of Duisburg-Essen, Germany

Outline • How to access information in the Semantic Web? • Faceted Search: An Introduction • Faceted Search in the Semantic Web • Facet Graphs • Evaluation • Discussion • Conclusion and Future Work

1. How to access information in the Semantic Web? • Common Web:Entering words in an input field (e.g. Google or Bing) • Problem ambiguity:Natural language is ambiguous!Finding the right information, however, requires the semantic of what should be searched to be specified by the user. • Solution:Artificial query languages like SPARQL that are uniquely defined. Access via SPARQL endpoints (e.g. DBpedia or the LOD cloud). SELECT DISTINCT ?object ?label WHERE { ?object rdf:type <URI of Football player> . ?object rdfs:label ?label . }

1. How to access information in the Semantic Web? • Problem required knowledge:SPARQL requires the language to be learned by the user (rather a task for experts). • Solution:Intuitive graphical interfaces to express search queries that are semantically unique but do not require any extra knowledge.

2. Faceted Search: An Introduction • Example: facets (3) update faceted search: result set (1) select facet (2) filter attribute

2. Faceted Search: An Introduction number of results facet category facet count (3) update facet attribute result set (1) select (1) select (2) filter Audiobooks Audiobooks

2. Faceted Search: An Introduction • Advantages: • Facets and their attributes are given (reduced effort) • Attributes are categoriezed (understanding) • No facet attribute can lead to an empty result set • Resulting number of resultsis shown in advance • Rapid update of result set (dynamic queries) • Selected attributes are shown and can be deselected

3. Faceted Search in the Semantic Web • mspace(Hearst et al. 2002: Finding the Flow in Web Site Search): Disadvantages: No facet count(number of results to expect) First order facets only (directly connected to the result set)

3. Faceted Search in the Semantic Web • Parallax (Huynh and Karger 2009: Parallax and companion: Set-based browsing for the Data Web): Advantage: Hierarchical facets possible(second or higher order facets) Disadvantages: Hierarchy not visible Browsing required

3. Faceted Search in the Semantic Web • Tabulator (Berners-Lee et al. 2008: Tabulator Redux: Browsing and writing Linked Data) : Advantages: Hierarchical facets Hierarchy on one page Disadvantages: Attributes get partitioned in different subtrees Redundant attributes

4. Facet Graphs • Idea: Facets and result set are represented as nodes in a graph visualization facets categories (labeled edges) Subject Year Theme Decade Story Title result set

4. Facet Graphs • How to extract facets from RDF data? result set facet ground type club1 venue1 type club ground type venue type club2 venue2 class objects properties class type venue3 1. Defining the result set class(e.g. German football club) 2. Extracting the facets (e.g. ground:Venue) objects result set facet

4. Facet Graphs • Prototypical implementation: gFacet

4. Facet Graphs • gFacet: • Flash application (animation, independence, no installation) • SPARQL queries (standard, data set independent) • Open source (http://code.google.com/p/gfacet/) • General benefits of Facet Graphs: • Attributes for each facet are grouped into one node • All nodes are shown in a coherent presentation on one page • Semantic relations between the nodes are represented by labeled edges • Facets can be added and removed by the user (individual search interfaces)

4. Facet Graphs • Hierarchical facets: color-coding

4. Facet Graphs • Multiple selections:

4. Facet Graphs • Pivot operation:While using gFacet, users may change their minds about what they want to search.

5. Evaluation • Comparative study of Parallax and gFacet: Three different types of tasks: • Find two players who are playing for a certain club. • Find two cities where players who are playing for a certain club are born. • Find one player who is playing for a certain club and is born in a certain city. VS.

5. Evaluation comments to the statements ‘It was difficult to understand the relations between the information’ • Results: Three different types of tasks: • Find two players who are playing for a certain club. • Find two cities where players who are playing for a certain club are born. • Find one player who is playing for a certain club and is born in a certain city.

R3.5: Zooming functionalities that are capable of showing information in different levels of detail. 6. Discussion • Are existing faceted search tools capable of supporting the Information Seeking Process (ISP) (Kuhlthau 1988) in the Semantic Web?

7. Conclusion and Future Work • Facet Graphs: • Facets as nodes in a graph visualization: Direct representation of relationships between facets • Connected representation on one page • Hierarchical facets • Color-coding: Understanding and traceing filtering effects • Personalized search interface (add/remove facets) • gFacet: • Proof of concept • Can query arbitrary SPARQL endpoints (e.g. DBpedia) • Comparative study: Especially applicable for complex queries (Semanitc Web) • However: Control remains a challenging task!

7. Conclusion and Future Work • Future work: • Zooming functionalities + focus and context technique: to handle massive amounts of facets in one graph • Saving search interfaces: to share especially helpful combinations of facets to lower the barrier for new users Do you want to load a search interface to explore: Publications about the Semantic Web? German football clubs and their players? US cities?

Thank you for your attention. visit gFacet at http://gfacet.semanticweb.org

Facet Graphs: Complex Semantic Querying Made Easy