This document presents an approach for exploratory relationship search through hierarchical clustering. It aims to address the challenge of too many relationship search results by organizing them into a cluster hierarchy based on common relationship patterns. An evaluation with participants performing lookup and exploratory search tasks on DBpedia data found that the clustering approach outperformed simple listing and faceted categorization alternatives. User feedback suggested areas for improvement like more concise visualizations and cognitive support. The authors conclude it is a promising approach and future work could combine facets and clustering or explore alternatives.

1. Towards Exploratory Relationship
Search: A Clustering-Based Approach
Yanan Zhang, Gong Cheng, Yuzhong Qu
Nanjing University, China

2. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

3. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

4. Relationship search

5. Searching graph-structured data
relatonship = path

6. Too many results!

7. Exploratory relationship search
• Exploring a set of relationships interactively and continuously
faceted categories
(RelFinder)
clustering
(our solution: RelClus)

8. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

9. Challenges
• How to meaningfully label a cluster?
• How to make sense of a cluster hierarchy?
• How to measure similarity between clusters?
Agglomerative hierarchical clustering
• Initially: relationships  singleton clusters
• Then: progressively merge the most similar pair

10. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

11. Relationship pattern
• High-level abstraction of relationships
– Vertices: entities or classes
– Edges: properties (undirected)

12. How to meaningfully label a cluster?
• Using a leastest common relationship pattern
– Vertices: leastest common classes (or entities)
– Edges: leastest common properties
Person
P1
R4
R5
label({R4, R5}) = P1

13. How to make sense of a cluster hierarchy?
• subPatternOf (⊑)
– Vertices: s.t. subClassOf (or instance-type)
– Edges: s.t. subPropertyOf
P3
P2
P1
P2 ⊑ P3, P1 ⊑ P3

14. How to measure similarity between clusters?
• sim(Ci,Cj) = how many commonalities they share
which are exactly captured by label(Ci∪Cj)
– Measure: -log (probability of seeing label(Ci∪Cj))
i.e. the information content associated with label(Ci∪Cj)
– Probability estimation: based on the data set
P3
P2
P1

15. A running example
P3
P2
R1
R2
R3
P1
R4
R5

16. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

17. Design
• Data set: DBpedia
• Systems
– RList: just a list of all results
– RFacet: w/ faceted categories (similar to RelFinder)
– RClus: w/ hierarchical clustering (our solution)
• Participants and tasks
– 2 participants provide searh tasks
• 3 (well-defined) lookup tasks
• 3 (open) exploratory search tasks
– 15 participants carry out tasks
• Metrics
– Questionnaire
– SUS
– User feedback

18. Questionnaire results

19. Some inspiring user feedback
• Dislike deep hierarchies
• Expect more concise visualization
• Need more cognitive support

20. Performance testing

21. Outline
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Motivation
Challenges
Approach
Evaluation
Conclusion

22. Conclusion
• Goal: clustering-based exploratory relationship search
• Approach: pattern-centric
• Future work
– Combining faceted categories and hierarchical clustering
– Going beyond them