-1- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Facet Graphs: Complex Semantic Querying Made Easy
Philipp Heim1, Thomas Ertl1 and Jürgen Ziegler2
1 Visualization and Interactive Systems Group (VIS), University of Stuttgart, Germany2 Interactive Systems and Interaction Design, University of Duisburg-Essen, Germany
ESWC 20107th Extended Semantic Web ConferenceHeraklion, Greece – June 1-3, 2010
-2- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-3- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-4- Philipp Heim, Thomas Ertl, Jürgen Ziegler
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 .
}
-5- Philipp Heim, Thomas Ertl, Jürgen Ziegler
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.
-6- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-7- Philipp Heim, Thomas Ertl, Jürgen Ziegler
2. Faceted Search: An Introduction
Example:
facets
facet(1) select
attribute
result set
(2) filter
(3) update
faceted search:
-8- Philipp Heim, Thomas Ertl, Jürgen Ziegler
2. Faceted Search: An Introduction
(1) select
Audiobooks
result set
(3) update
(2) filter(1) select
Audiobooks
number of results
facetcategory
facet count
facet attribute
-9- Philipp Heim, Thomas Ertl, Jürgen Ziegler
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
-10- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-11- Philipp Heim, Thomas Ertl, Jürgen Ziegler
3. Faceted Search in the Semantic Web
mspace (Hearst et al. 2002: Finding the Flow in Web Site Search):
Disadvantages: 1.No facet count(number of results to expect)2.First order facets only (directly connected to the result set)
-12- Philipp Heim, Thomas Ertl, Jürgen Ziegler
3. Faceted Search in the Semantic Web
Parallax (Huynh and Karger 2009: Parallax and companion: Set-based
browsing for the Data Web):
Advantage:1.Hierarchical facets possible(second or higher order facets)
Disadvantages: 1.Hierarchy not visible2.Browsing required
-13- Philipp Heim, Thomas Ertl, Jürgen Ziegler
3. Faceted Search in the Semantic Web
Tabulator (Berners-Lee et al. 2008: Tabulator Redux: Browsing and
writing Linked Data) :
Advantages:1.Hierarchical facets2.Hierarchy on one page
Disadvantages: 1.Attributes get partitioned in different subtrees2.Redundant attributes
-14- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-15- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs Idea: Facets and result set are represented as nodes in a
graph visualization
Theme SubjectYearDecade Story Title
result set
facets
categories(labeled edges)
-16- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs
How to extract facets from RDF data?
club2
club1club
type
type
result set
class objects
venue2
venue1
venue
type
type
ground
ground
venue3 type
facet
properties
objects
class
facetresult set
1. Defining the result set class(e.g. German football club)
2. Extracting the facets(e.g. ground:Venue)
-17- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs Prototypical implementation: gFacet
-18- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs
gFacet:
1. Flash application (animation, independence, no installation)
2. SPARQL queries (standard, data set independent)
3. Open source (http://code.google.com/p/gfacet/)
General benefits of Facet Graphs:
1. Attributes for each facet are grouped into one node
2. All nodes are shown in a coherent presentation on one page
3. Semantic relations between the nodes are represented by labeled edges
4. Facets can be added and removed by the user (individual search interfaces)
-19- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs
Hierarchical facets:
color-coding
-20- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs
Multiple selections:
-21- Philipp Heim, Thomas Ertl, Jürgen Ziegler
4. Facet Graphs
Pivot operation:While using gFacet, users may change their minds about what they want to search.
-22- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-23- Philipp Heim, Thomas Ertl, Jürgen Ziegler
5. Evaluation
Comparative study of Parallax and gFacet:
Three different types of tasks:
1. Find two players who are playing for a certain club.
2. Find two cities where players who are playing for a certain club are born.
3. Find one player who is playing for a certain club and is born in a certain city.
VS.
-24- Philipp Heim, Thomas Ertl, Jürgen Ziegler
5. Evaluation
Results:
Three different types of tasks:
1. Find two players who are playing for a certain club.
2. Find two cities where players who are playing for a certain club are born.
3. Find one player who is playing for a certain club and is born in a certain city.
comments to the statements ‘It was difficult to understand the relations
between the information’
-25- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-26- Philipp Heim, Thomas Ertl, Jürgen Ziegler
6. Discussion
Are existing faceted search tools capable of supporting the Information Seeking Process (ISP) (Kuhlthau 1988) in the Semantic Web?
R3.5: Zooming functionalities that are capable of showing information in different levels of detail.
-27- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Outline
1. How to access information in the Semantic Web?
2. Faceted Search: An Introduction
3. Faceted Search in the Semantic Web
4. Facet Graphs
5. Evaluation
6. Discussion
7. Conclusion and Future Work
-28- Philipp Heim, Thomas Ertl, Jürgen Ziegler
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!
-29- Philipp Heim, Thomas Ertl, Jürgen Ziegler
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
German football clubs and their players?
Do you want to load a search interface to explore:
Publications aboutthe Semantic Web?
US cities?
-30- Philipp Heim, Thomas Ertl, Jürgen Ziegler
Thank you for your attention.
visit gFacet at http://gfacet.semanticweb.org