Browsing-oriented Semantic Faceted Search
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Browsing-oriented Semantic Faceted Search

on

  • 1,531 views

 

Statistics

Views

Total Views
1,531
Views on SlideShare
1,531
Embed Views
0

Actions

Likes
4
Downloads
24
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Browsing-oriented Semantic Faceted Search Presentation Transcript

  • 1. Browsing-oriented Semantic Faceted Search Andreas Wagner, Günter Ladwig and Duc Thanh TranInstitute of Applied Informatics and Formal Description Methods (AIFB)KIT – University of the State of Baden-Wuerttemberg andNational Research Center of the Helmholtz Association www.kit.edu
  • 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 Results2 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 3. INTRODUCTION & MOTIVATION3 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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. Fuzzy Need Example 2 Precise Need Susan is a grad-student. She is wishes to find information about Knuth’s first book Funda- See, e.g., [1,2]. mental Algorithms.4 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 5. Faceted Search 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].5 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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 values6 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 7. Faceted Search in a Semantic Web Context Facet Operations Focus Selection Query Result Refinement Modifaction Exploration Expansion Focus Selection Initial Query knows name ?x ?y “Knuth“ works at Expansion Refinement “Stanford University“7 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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). See, e.g., [4,5,6,7]. Example 1 Susan is a novice computer science student. She is wishes to find information about work of prestigious computer scientists. Fuzzy Need Example 2 Precise Need Susan is a grad-student. She is wishes to find information about Knuth’s first book Funda- mental Algorithms.8 Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 9. Our Contributions Fuzzy Need Challenges? Precise Need 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? Contributions State-of-the-Art? See, e.g., [5,8,9]. Restricted Facet (Facet Value) Grouping Browsing- Grouping focuses on facets only, no (flexible) means for oriented Facet grouping large facet value spaces. (Value) Spaces Search-oriented Facet Ranking Browsing-oriented Existing ranking approaches assume a precise Facet Ranking information need (or are generic). See, e.g., [4,5,6,7].9 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 10. BROWSING-ORIENTED FACETED SEARCH10 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 11. Challenges? 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?11 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 12. 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]. Result Set (Set of Computer mary Science Professors) ann P2 name paul 70 P1 works at 150 P4 U2 U1 age 250 P3 U4 Focus 300 U312 Selection Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 13. Browsing-oriented Facet and Facet Value Other Facet Operations Spaces Focus Selection Refinement Facet Operation: Browsing Expansion 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. Result Set (Set of Computer Science Professors) [ann − paul] ...? Compact, P2 name intensional representation of P1 the facet space. P4 works at ?u age [70− 300] ...? P3 Focus13 Selection Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 14. 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. Result Set (Set of Computer ann Science Professors) [ann − paul] mary P2 name [mary - paul] paul P1 P4 works at Compact, ?u age [70− 300] ... intensional P3 representation of the facet and14 Andreas Wagner, Günter Ladwig, Duc Thanh Tran facet value space. Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 15. 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 evaluation15 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 16. Challenges? 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?16 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 17. 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. Small Steps Uniform Steps Metric Metric Metric Metric Comprehensible Result Segments Metric Metric17 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 18. 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. Facet Extended Facet Tree ann name [ann − paul] mary [mary - paul] paul18 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 19. Browsing-oriented Facet Ranking – Intuition Idea Via small result modifications, users get to know the Small Steps result set bit by bit. Small changes can be comprehended more easily by users. 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.19 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 20. Browsing-oriented Facet Ranking – Intuition Idea Uniform We consider query modifications to be non-uniform, Steps 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. 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.20 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 21. Browsing-oriented Facet Ranking – Intuition Example: Binding Segment Size Balance Uniform Steps Facet: name Facet Path: works at, age [ann-paul] P1 P2 P3 P4 P1 P2 P3 P4ann [mary-paul] [70-300] P1 P2 P3 P4 P1 P2 P3 P4 P1 [250-300] [70-150] mary P1 P3 P2 P4 P2 P3 P1 P4 70 150 paul Binding Segment Tree 250 P1 P3 P2 P421 Andreas Wagner, Günter Ladwig, Duc Thanh Tran 300 Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 22. Browsing-oriented Facet Ranking – Intuition Idea Comprehensible Result For users who are unfamiliar with a result set, it is Segments important that a facet operation leads to obvious and comprehensible result modifications. Metrics Binding Distinguishability A facet has a high distinguishability, when it leads to facet values that precisely identify variable bindings. See [4]. 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.22 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 23. EVALUATION23 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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]24 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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)?25 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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.26 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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)?27 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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.28 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 29. CONCLUSION29 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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?30 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 31. 31 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 32. REFERENCES32 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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, 2009.33 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 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/34 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 35. BACKUP SLIDES35 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 36. Faceted Search – Terminology What are facets? Conceptual dimensions of the current result set. What are facet values? Values of conceptual dimensions. Dimension Search Result Facets Facet Values36 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 37. Browsing-oriented Facet and Facet Value Spaces Example: Facet Tree and Browsing37 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)
  • 38. Browsing-oriented Facet and Facet Value Spaces Example: Extended Facet Tree38 Andreas Wagner, Günter Ladwig, Duc Thanh Tran Institute of Applied Informatics and Formal Description Methods (AIFB)