browsing-oriented semantic faceted search

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)

2

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



3

INTRODUCTION & MOTIVATION




4

User Information Need


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].



5

Faceted Search


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



6

Faceted Search in a Semantic Web Context


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



7

Faceted Search in a Semantic Web Context


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



8

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].



9

Our Contributions


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



10

BROWSING-ORIENTED

FACETED SEARCH




11 Andreas Wagner, Günter Ladwig, Duc Thanh Tran




Challenges?




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



13

[ann − paul]


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

...?

...?


Other Facet Operations

Focus Selection

Refinement

Expansion



14

[ann − paul]


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.


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.



15


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








Challenges?



17


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.


Small Steps Uniform Steps

Comprehensible Result

Segments

MetricMetric

MetricMetric

MetricMetric



18


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.


[ann − paul]

ann

[mary - paul]

mary

paul

name

Facet Extended Facet Tree



19

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.


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.



20


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.


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.



21


Example: Binding Segment Size Balance


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



22


Idea

For users who are unfamiliar with a result set, it is

important that a facet operation leads to obvious and

comprehensible result modifications.


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].



23

EVALUATION




24

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]




25

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)?




26

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.




27

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)?




28

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.




29

CONCLUSION




30

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?




32

REFERENCES




33

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.




34

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/




35

BACKUP SLIDES




36

Faceted Search – Terminology


What are facets?

Conceptual dimensions of the current result set.

What are facet values?

Values of conceptual dimensions.

Search Result

Dimension

Facets Facet Values



37


Spaces

Example: Facet Tree and Browsing




38


Spaces

Example: Extended Facet Tree


browsing-oriented semantic faceted search

Education

restricted facet facet

facet space

gnter ladwig

large facet value sets

multiple facet selections

formaldescription methods

highdimensional facet

values6andreas wagner