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KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association www.kit.edu
Institute of Applied Informatics and Formal Description Methods (AIFB)
Browsing-oriented Semantic Faceted Search
Andreas Wagner, Günter Ladwig and Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Agenda
Introduction and Motivation
Information Needs
Faceted Search Concepts
Contributions
Browsing-oriented Faceted Search …
Browsing-oriented Facet and Facet Value Spaces
Browsing-oriented Facet Ranking
Evaluation Results
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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INTRODUCTION & MOTIVATION
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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User Information Need
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
User Need
... ... ...
Information
Example 1
Susan is a novice computer science student.
She is wishes to find information about work
of prestigious computer scientists.
Example 2
Susan is a grad-student. She is wishes to find
information about Knuth’s first book Funda-
mental Algorithms.
Fuzzy Need
Precise Need
See, e.g., [1,2].
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Faceted Search
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Faceted Search is…
a paradigm allowing users to
explore a data source through
fluent interaction of refinement and
expansion.
See, e.g., [3].
... ... ...Faceted
Search
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Faceted Search in a Semantic Web Context
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Query and Data Model
Data model is a graph
Query model based on basic graph-patterns
Facet Model
Facets with are edge labels of (one ore more) node(s) contained in
the current result set
Nodes of these edges are facet values
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Description Methods (AIFB)
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Faceted Search in a Semantic Web Context
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Facet Operations
Focus Selection
Refinement
Expansion
?x ?y “Knuth“knows name
Query
Modifaction
works at
“Stanford University“
Refinement
Initial Query
Expansion
Focus Selection
Result
Exploration
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Our Contributions
Browsing-oriented Faceted Search
Fuzzy information needs require different kinds of facets, and a
different grouping of facets. Strong need for browsing.
State-of-the-art focuses mainly on precise needs (or target a
generic scenario).
Example 1
Susan is a novice computer science student.
She is wishes to find information about work
of prestigious computer scientists.
Example 2
Susan is a grad-student. She is wishes to find
information about Knuth’s first book Funda-
mental Algorithms.
Fuzzy Need
Precise Need
See, e.g., [4,5,6,7].
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Our Contributions
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
How to handle high-dimensional facet values for browsing?
How to handle large facet value sets for browsing?
Facet & facet value ranking well-suited for browsing?
Restricted Facet (Facet Value) Grouping
Grouping focuses on facets only, no (flexible) means for
grouping large facet value spaces.
Search-oriented Facet Ranking
Existing ranking approaches assume a precise
information need (or are generic).
Challenges?
State-of-the-Art?
See, e.g., [4,5,6,7].
See, e.g., [5,8,9].
Browsing-
oriented Facet
(Value) Spaces
Browsing-oriented
Facet Ranking
Contributions
Fuzzy Need
Precise Need
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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BROWSING-ORIENTED
FACETED SEARCH
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
11 Andreas Wagner, Günter Ladwig, Duc Thanh Tran
How to handle high-dimensional facet values for browsing?
How to handle large facet value sets for browsing?
Facet & facet value ranking well-suited for browsing?
Challenges?
Institute of Applied Informatics and Formal
Description Methods (AIFB)
12 Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Browsing-oriented Facet and Facet Value
Spaces
Facet Tree (FT)
A facet tree (i.e., hierarchical grouping of facets) is derived from
nodes and edges of the data graph, which are reachable from the
result set.See, e.g., [8,9,10].
P2
Result Set
(Set of Computer
Science Professors)
P1
P3
P4
ann
mary
paul
name
U2
U4
U3
U1
works at150
70
250
300
age
Focus
Selection
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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[ann − paul]
Browsing-oriented Facet and Facet Value
Spaces
Facet Operation: Browsing
Browsing consists of (multiple) facet selections. However, facets
selected during browsing are not evaluated, i.e., the underlying
query does not change and thus the result set is not modified.
P2
Result Set
(Set of Computer
Science Professors)
P1
P3
P4
name
?u
works at
age [70− 300]
Compact,
intensional
representation of
the facet space.
Focus
Selection
...?
...?
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Other Facet Operations
Focus Selection
Refinement
Expansion
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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[ann − paul]
Browsing-oriented Facet and Facet Value
Spaces
Extended Facet Tree
Employ clustering to extend the facet tree. Leaf nodes in the facet
tree containing more data values than a given threshold are
clustered, resulting in a set of data value trees.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
P2
Result Set
(Set of Computer
Science Professors)
P1
P3
P4
name
?u
works at
age [70− 300]
ann
[mary - paul]
mary
paul
...Compact,
intensional
representation of
the facet and
facet value space.
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Description Methods (AIFB)
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Browsing-oriented Facet and Facet Value
Spaces
Extended Facet Tree
We currently employ a simply divisive, hierarchical clustering.
Depending on the application setting, other clustering algorithms may
be better suited
Highlight outliers
Highlight expected values
...
Benefits
Entire facet and facet value space is (compactly) represented
User may drill-down, depending on how precise (fuzzy) her need is
Drawbacks
More interaction is needed, as facet tree is more fine-grained
See evaluation
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
16 Andreas Wagner, Günter Ladwig, Duc Thanh Tran
How to handle high-dimensional facet values for browsing?
How to handle large facet value sets for browsing?
Facet & facet value ranking well-suited for browsing?
Challenges?
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Description Methods (AIFB)
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Browsing-oriented Facet Ranking
A browsing-oriented ranking function incorporates different
notions (via their metrics): Small steps, uniform steps,
comprehensible result segments.
Notions (metrics) influence each other.
Depending on the application scenario, only a subset of the
notions (metrics) may suffice.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Small Steps Uniform Steps
Comprehensible Result
Segments
MetricMetric
MetricMetric
MetricMetric
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Description Methods (AIFB)
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Browsing-oriented Facet Ranking
Idea
For ranking a facet f, consider the facet and facet value space that
can be reached via f and result set modifications, which can be
performed via facet paths originating from f .
Use the extended facet tree, associated with a facet f, for assessing
the browsing quality of f.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
[ann − paul]
ann
[mary - paul]
mary
paul
name
Facet Extended Facet Tree
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Browsing-oriented Facet Ranking – Intuition
Idea
Via small result modifications, users get to know the
result set bit by bit.
Small changes can be comprehended more easily by
users.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Small Steps
Metrics
Maximum Height
The height of the extended FT, directly reflects the maximum number of
possible facet operations.
Minimum Branching Factor
Trees with small branching factor lead to smaller result
modifications, as such trees tend to be higher.
A small branching factor reflects a small number of possible user
decisions.
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Description Methods (AIFB)
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Browsing-oriented Facet Ranking – Intuition
Idea
We consider query modifications to be non-uniform,
when they have varying impacts on the result set size.
When browsing, it is hard for users to choose
between non-uniform query modifications.
Such query modifications can be confusing and may
lead to irrelevant results.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Uniform Steps
Metrics
Height Balance
The extended FT is perfectly height balanced, when all leaves
are of equal edge distance to the root.
Facet Value Set & Binding Segment Size Balance
Balance the size balance w.r.t. facet value sets and binding set
segments, which may be reached via the extended FT.
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Browsing-oriented Facet Ranking – Intuition
Example: Binding Segment Size Balance
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Uniform Steps
P2P1 P3 P4
Facet: name Facet Path: works at, age
P1
P2P1 P3 P4
P2P1 P3 P4P2P1 P3 P4
P2 P3 P1 P4P1 P3 P2 P4
P1 P3 P2 P4
[ann-paul]
ann [mary-paul]
paul
mary
[70-300]
[250-300] [70-150]
250
300
70 150
Binding Segment Tree
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Browsing-oriented Facet Ranking – Intuition
Idea
For users who are unfamiliar with a result set, it is
important that a facet operation leads to obvious and
comprehensible result modifications.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Comprehensible Result
Segments
Metrics
Binding Distinguishability
A facet has a high distinguishability, when it leads to facet values
that precisely identify variable bindings.
Minimal Binding Segment Overlap
Binding segments with minimal overlaps are preferred to ensure
that facet operations along a facet tree lead to different result
modifications.
See [4].
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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EVALUATION
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Evaluation – Setting
We conducted a task-based user evaluation.
Participants
24 participants
Mixed group: 18 participants had a computer science background, 6
had non-technical background
Tasks: 24 tasks were chosen by domain experts and comprised
both precise and fuzzy information needs.
Data: we used the (complete) DBpedia dataset [11]
System: based on Information Workbench [12]
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Evaluation – Extended Facet Tree
Tasks
Four tasks (C1-C4) for investigating the effects of our data value trees
Eight complex browsing tasks (B1-B8), to assess the quality of
browsing based on the facet tree
Baseline
System with a flat list of facets and no data value trees
We designed clustering (C) and browsing (B) tasks in a way, that we
were able to compare the effects of data value clustering en- or
disabled and facets grouped in lists or trees
How effective and how efficient is the extended facet tree (com-
pared to the baseline)?
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Evaluation – Extended Facet Tree
Results
Results suggest that the use of our extended facet tree improves the
efficiency and effectiveness of the task completion, concerning
complex, fuzzy tasks.
Search is more efficient and equally effective, with regard to precise
and simple needs only.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Evaluation – Browsing-oriented Ranking
Tasks
Find (F) tasks comprise of 8 tasks (F1-F8), which involve precise and
fuzzy information needs. Goal is to find a concrete item of interest.
Explore (E) tasks comprises of 4 tasks (E1-E4), where users had to
explore a result set (fuzzy need), i.e., find outliers, interesting or
strange results.
Baseline: a system employing search-oriented ranking.
How effective and how efficient is the browsing-oriented ranking
(compared to the baseline)?
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Evaluation – Browsing-oriented Ranking
Results
While browsing-oriented ranking might not provide an efficient way to
an item of interest, it is suitable for scenarios with no precise need
and large result sets to be explored.
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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CONCLUSION
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Conclusion & Future Work
Current faceted search approaches imply a precise information
need (or are generic) and thus, focus on the search paradigm.
We target the browsing paradigm, where users only vaguely know
the domain or item of interest.
Our solution outperformed the state-of-the-art w.r.t. fuzzy infor-
mation needs.
Future Work …
Efficiency aspects?
When to switch between search- and browsing-oriented ranking?
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Institute of Applied Informatics and Formal
Description Methods (AIFB)
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REFERENCES
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
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Description Methods (AIFB)
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References
1. G. Marchionini and B. Shneiderman. Finding facts vs. browsing knowledge in hy-
pertext systems. Computer, 21(1):70–80, 1988.
2. G. Marchionini. Exploratory search: from finding to understanding. Commun. ACM,
49(4):41–46, 2006.
3. M. Hearst, K. Swearingen, K. Li, and K.-P. Yee. Faceted metadata for image
search and browsing. In CHI, pages 401–408. ACM, 2003.
4. S. Basu Roy, H. Wang, G. Das, U. Nambiar, and M. Mohania. Minimum-effort
driven dynamic faceted search in structured databases. In CIKM, pages 13–22.
ACM, 2008.
5. W. Dakka, P. G. Ipeirotis, and K. R. Wood. Automatic construction of multifaceted
browsing interfaces. In CIKM, pages 768–775. ACM, 2005.
6. D. Dash, J. Rao, N. Megiddo, A. Ailamaki, and G. Lohman. Dynamic faceted
search for discovery-driven analysis. In CIKM, pages 3–12. ACM, 2008.
7. J. Koren, Y. Zhang, and X. Liu. Personalized interactive faceted search. In WWW,
pages 477–486. ACM, 2008.
8. P. Heim, T. Ertl, and J. Ziegler. Facet graphs: Complex semantic querying made
easy. In ESWC, pages 288–302. Springer, 2010.
9. D. F. Huynh and D. R. Karger. Parallax and companion: Set-based browsing for
the data web. In WWW, 2009.
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Description Methods (AIFB)
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References
10. T. Berners-Lee, Y. Chen, L. Chilton, D. Connolly, R. Dhanaraj, J. Hollenbach, A.
Lerer, and D. Sheets. Tabulator: Exploring and analyzing linked data on the se-
mantic web. In Proceedings of the 3rd International Semantic Web User Interaction
Workshop, 2006.
11. C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S.
Hellmann. Dbpedia - a crystallization point for the web of data. Journal of Web
Semantics, 7(3):154–165, 2009.
12. http://iwb.fluidops.net/
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Description Methods (AIFB)
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BACKUP SLIDES
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Faceted Search – Terminology
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
What are facets?
Conceptual dimensions of the current result set.
What are facet values?
Values of conceptual dimensions.
Search Result
Dimension
Facets Facet Values
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Browsing-oriented Facet and Facet Value
Spaces
Example: Facet Tree and Browsing
Andreas Wagner, Günter Ladwig, Duc Thanh Tran
Institute of Applied Informatics and Formal
Description Methods (AIFB)
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Browsing-oriented Facet and Facet Value
Spaces
Example: Extended Facet Tree
Andreas Wagner, Günter Ladwig, Duc Thanh Tran