Presented at ISWC 2014, Riva del Garda, Italy.

1. Explass: Exploring Associations between Entities
via Top-K Ontological Patterns and Facets
Gong Cheng, Yanan Zhang, Yuzhong Qu
Websoft Research Group
State Key Laboratory for Novel Software Technology
Nanjing University, China

2. Association search

3. Association search
?
air pollution ? autism
?

4. Association search
You
?
?
?
?

5. Association search on the Web of documents
associations hidden in text

6. Association search on an entity-relation graph
paper-A conf-A
Alice Bob
article-A
conf-B
paper-B
paper-C
paper-D
inProcOf
secondAuthor reviewer
chair
firstAuthor
firstAuthor inProcOf
secondAuthor cites
cites
extends
firstAuthor
associations exposed as graph

7. association = path
Alice Bob
paper-A conf-A secondAuthor inProcOf reviewer
paper-B conf-B firstAuthor inProcOf chair
paper-B paper-C firstAuthor cites firstAuthor
paper-D paper-C secondAuthor cites firstAuthor
paper-D article-A secondAuthor extends firstAuthor

8. Challenge
over 1,000 associations
in DBpedia
(within 4 hops)
How to explore them?

9. Exploration methods (1)
• Clustering
• Facets

10. cluster = pattern
Common
super-property Common class
Paper Conference author inProcOf role
paper-A conf-A secondAuthor inProcOf reviewer
paper-B conf-B firstAuthor inProcOf chair
Position 1 Position 2 Position 3 Position 4 Position 5
pattern
match
associations

11. Problem: To recommend k patterns
paper-A conf-A secondAuthor inProcOf reviewer
paper-B conf-B firstAuthor inProcOf chair
paper-B paper-C firstAuthor cites firstAuthor
paper-D paper-C secondAuthor cites firstAuthor
paper-D article-A secondAuthor extends firstAuthor

12. Step 1: Mining all significant patterns
Paper Conference author inProcOf role
frequency = 2/5 > threshold
paper-A conf-A secondAuthor inProcOf reviewer
paper-B conf-B firstAuthor inProcOf chair
paper-B paper-C firstAuthor cites firstAuthor
paper-D paper-C secondAuthor cites firstAuthor
paper-D article-A secondAuthor extends firstAuthor

13. Formulated as frequent itemset mining
1. transaction = association
item = <position, class> or <position, property>
2. Mining frequent itemsets
3. itemset  pattern
paper-A conf-A secondAuthor inProcOf reviewer
<1, secondAuthor>
<1, author>
<2, ConfPaper>
<2, Paper>
<3, inProcOf> <4, Conference> <5, reviewer>
<5, role>
Position 1 Position 2 Position 3 Position 4 Position 5

14. Formulated as frequent itemset mining
1. transaction = association
item = <position, class> or <position, property>
2. Mining frequent itemsets
3. itemset  pattern
paper-A conf-A secondAuthor inProcOf reviewer
<1, author>
<2, ConfPaper>
<2, Paper>
<3, inProcOf> <4, Conference>
<5, role>
Position 1 Position 2 Position 3 Position 4 Position 5

15. Formulated as frequent itemset mining
1. transaction = association
item = <position, class> or <position, property>
2. Mining frequent itemsets
3. itemset  pattern
paper-A conf-A secondAuthor inProcOf reviewer
<1, author>
<2, ConfPaper>
<2, Paper>
<3, inProcOf> <4, Conference>
<5, role>
Paper Conference author inProcOf role

16. Step 2: Finding k frequent, informative, and
small-overlapping patterns
• Frequency (as previous)
• Informativeness
• Overlap

17. Step 2: Finding k frequent, informative, and
small-overlapping patterns
• Frequency (as previous)
• Informativeness
• informativeness of a class = self-information of its occurrence
(more informative = having fewer instances)
e.g. ConfPaper > Paper
• informativeness of a property = entropy of its values
(more Informative = having more diverse values)
e.g. is-author-of > nationality
• Overlap
Paper Conference author inProcOf role

18. Step 2: Finding k frequent, informative, and
small-overlapping patterns
• Frequency (as previous)
• Informativeness
• Overlap
• Ontological overlap: holding subClassOf/subPropertyOf relations
• Contextual overlap: matched by common associations in the results
ConfPaper Conference author inProcOf role
ontological
overlap
Paper Paper firstAuthor cites author

19. Formulated as multidimensional 0-1 knapsack
• Find k patterns that
maximize frequency*Informativeness (goal)
and not share considerably large overlap (constraints)
• Solved by a greedy algorithm

20. Exploration methods (2)
• Clustering
• Facets
• facet values = classes of entities and properties
appearing in associations in the results
• Problem: To recommend k facet values
(solved in a similar way)
ConfPaper Paper Conference
paper-A conf-A secondAuthor inProcOf reviewer

21. Demo based on DBpedia
ws.nju.edu.cn/explass

22. Demo based on DBpedia
ws.nju.edu.cn/explass
facet values
(classes)
facet values
(properties)

23. Demo based on DBpedia
ws.nju.edu.cn/explass
a collapsed
pattern
an expanded
pattern
associations not matching
any pattern above

24. User study
• 26 association exploration tasks over DBpedia
• Derived from QALD queries and
“People also search for”
• Example: Suppose you will write an article
about the associations between Abraham
Lincoln and George Washington. Use the given
system to explore their associations and
identify several themes to discuss in the article.
• 20 subjects
• 3 approaches
• Explass: clustering + facets
• RelClus: clustering into a hierarchy of patterns
• RF: facets only (similar to RelFinder)
from QALD

25. Post-task questionnaire results

26. Usability scores (SUS)

27. User behavior

28. Conclusion
1. Provide patterns wisely.
• To avoid deep, complicated hierarchy
• To avoid very general, almost meaningless concepts
2. Combine patterns and facets wisely.
• Patterns as meaningful summaries of results
• Facets as filters for refining the search
Filters Summaries of results

29. Future work
• Performance optimization
• (online) path finding
• (online) frequent itemset mining
• Exploring associations between several entities
or, a data set

30. Questions?