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

2. Introduction
Related Work
Exploratory Relationship Search
Approach
Experiments
Conclusion

3. Introduction How is Sydney related to Melbourne?
Semantic Web: a Giant Global Graph
Relationships->paths (path finding)
A large number of relationships:
Browsing becomes a nontrivial task.
Exploratory search
Provide users with services beyond lookup and ranking, to facilitate cognitive processing and interpretation of the results via continuous and exploratory interaction.

4. Related Work
Relationship Search
Path Finding
Relationship Ranking

5. Exploratory Search learn, investigate Search activities: lookup,
Complex information needs
Human participation
Existing efforts mainly focus on organizing search results into meaningful groups.
Facet categories
Clustering: let the search results speaks for themselves.
learn, investigate

6. Relationship Clustering
Similarity Measurement
Relationships are structured.
Cluster Labeling
Central topic and common structural characteristics.
Intuitiveness of Sub-hierarchies
Well-defined.

7. Approach Measuring Similarity
Based on how many commonalities they share.
R1, R2, R3:
Australia
Person: Thomas Keneally
Lleyton Hewitt
John Gorton
Victoria(Australia)
birthPlace
Athlete: Leslie Cody
William Bowrey
R4, R5:

8. Approach Labeling a Cluster By commonalities. Relationship pattern.
Holding the most commonalities
Merge clusters labeled with relationship patterns and build a hierarchy.
Building a Hierarchy

9. Relationship Relationship pattern Label of a Cluster v0a1v1a2v2…anvn
c0p1c1p2v2…pncn
I(e)={e}
Label of a Cluster
Information content
-log p(c0p1c1p2v2…pncn)
Conditional independence assumption
=-log p(c0)p(p1)…p(pn)p(cn)

10. Experiments Data Set User Study DBpedia 3.7
Ontology Infobox Properties
Ontology Infobox Types
Ontology Ontology
A data graph
edges.
365 classes.
entities.
User Study
Compare with two baseline approaches.
Discuss its effectiveness and usability.
Performance Testing
scalability

11. User Study
Tasks
Systems
RClus
Rlist
Rfacet
Participants
15 students

12. User Feedback Rlist RFacet RClus
Easy to understand. 5
Hardly useful. 7
RFacet
Enjoy filter 3
Dislike click-to-exclude manner 9
Never use any filter(learn) 6
Beneficial in particular in lookup tasks, but requires careful design.
RClus
Useful to overview 5
Dislike the deep hierarchies 4
Preferable in complex search tasks with large results to browse, but deep hierarchies should be avoided.

13. Performance Testing

14. Conclusion
Proposed an approach to realize clustering-based exploratory relationship search.
Users interact with the generated hierarchical clustering as if they start with an overview of the results and continuously, according to their needs, refine or coarsen a path query under semantic constraints at different levels of granularity, which conforms with human intuition.
Other ways to measure similarity.
Optimize the performance.
Index structures
More efficient approximation
Other interactive methods.

15. Thanks!