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

Introduction Related Work Exploratory Relationship Search Approach Experiments Conclusion

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.

Related Work Relationship Search Path Finding Relationship Ranking

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

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

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:

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

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)

Experiments Data Set User Study DBpedia 3.7 Ontology Infobox Properties Ontology Infobox Types Ontology Ontology A data graph 9105118 edges. 365 classes. 2210233 entities. User Study Compare with two baseline approaches. Discuss its effectiveness and usability. Performance Testing scalability

User Study Tasks Systems RClus Rlist Rfacet Participants 15 students

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.

Performance Testing

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.

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