1. Discovering Neighborhood Pattern Queries by Sample Answers in Knowledge Base
Jialong Han1, Kai Zheng2, Aixin Sun1, Shuo Shang3, and Ji-Rong Wen4
1 Nanyang Technological University
2 The University of Queensland
3 China University of Petroleum (Beijing)
4 Renmin University of China
31/12/2018
ICDE 2016, Helsinki, Finland

2. Knowledge Bases and Structural Queries
Knowledge bases: DBpedia, Freebase, YAGO, NELL, etc.
Viewed as graphs
Queried by structural queries
SPARQL for RDF
MQL for Freebase
Cypher for neo4j
Which chess player was born and died in the same place ?
SELECT ?uri
WHERE {
?uri :type :ChessPlayer .
?uri :birthPlace ?place .
?uri :deathPlace ?place }
Complete Answers
M. Botvinnik
P. Morphy …
31/12/2018
ICDE 2016, Helsinki, Finland

3. Structural Query Discovery: Motivation
It is always not easy for a user to write structural queries.
She needs to follow the syntax;
She needs to be familiar with types/relations used in the KB.
Can we automatically find structural queries
based on representative partial answers?
Which chess player was born and died in the same place?
SELECT ?uri
WHERE {
?uri :type :ChessPlayer .
?uri :birthPlace ?place .
?uri :deathPlace ?place }
Which chess player was born and died in the same place?
Complete Answers
M. Botvinnik
P. Morphy …
Representative Partial Answers
M. Botvinnik ？
31/12/2018
ICDE 2016, Helsinki, Finland

4. Motivating Example
We concentrate on Neighborhood Pattern Queries (NPQ).
One “pivot”.
Does not involve numeric ops, regular expressions, etc.
Given example entities 𝐼 from the user, all NPQs can be classified into three kinds.
Irrelevant: results does not cover 𝐼;
Not relevant enough: results cover 𝐼 but does not rank them high;
Relevant: 𝐼 is ranked high in the results.
Popularity Order
Query (a)
Query (b)
Query (c)
B. Obama
E. Lasker
V. Putin
M. Botvinnik
P. Morphy
G. Kasparov
Rank: +∞
Rank: 4
Rank: 1
31/12/2018
ICDE 2016, Helsinki, Finland

5. Problem Statement and Solution Overview
Reverse Top-k Neighborhood Pattern Queries (RkNPQ)
Given a knowledge base 𝐷 and a popularity order ≺ on 𝑉 𝐷 , for input nodes 𝐼⊆𝑉 𝐷 , find all neighborhood pattern queries 𝑞 s.t.
𝐷 𝑞 ⊇𝐼, and
when ranking 𝐷 𝑞 according to ≺, nodes in 𝐼 all appear in the top-k results.
Solution: filter and refine.
Filter: generate all NPQs satisfying 1;
Refine: eliminate all generated NPQs violating 2.
31/12/2018
ICDE 2016, Helsinki, Finland

6. The Filtering Stage Perform level-wise search on the query space.
Start with the simplest shapes of NPQs (single node or edge).
Generate complicated ones through Extend and Join on simple ones.
Completeness guaranteed by [Han, CIKM’13].
Terminate a branch if condition 1 is violated.
𝐼 = { M. Botvinnik }
31/12/2018
ICDE 2016, Helsinki, Finland

7. Trivial Refine
Execute all NPQs generated by the filter stage, and test for condition 2.
Use SPARQL or graph query engines like neo4j, gStore, and JENA-TDB.
Drawbacks: unnecessary or redundant computations are not removed.
We propose three optimizations on this stage.
𝐼 = { M. Botvinnik }
31/12/2018
ICDE 2016, Helsinki, Finland

8. Refine Optimization 1: Shared Evaluation
Observation 1: 𝐷 𝑞 of different 𝑞 overlap with each other.
For q 1 , q 2 , q 1 is a sub-query of q 2 , we have 𝐷(𝑞 1 )⊇𝐷( 𝑞 2 ).
Maintain 𝐷 𝑞 by (intersecting and) verifying results of sub-queries.
𝐼 = { M. Botvinnik }
31/12/2018
ICDE 2016, Helsinki, Finland

9. Refine Optimization 2: Indicator Answers
Observation 2: To verify 𝑞, 𝐷 𝑞 needs not be completely evaluated.
Define indicator answers 𝐼𝐴 𝑞 = 𝑣 𝑣∈𝐷 𝑞 ∧𝑣≺ 𝑖𝑛𝑓 𝐼 ∧𝑣∉𝐼 .
Only nodes in 𝐼𝐴 𝑞 affect the Top-k condition.
𝑞 meets the Top-k condition iff. 𝐼𝐴 𝑞 ≤𝑘−|𝐼|.
Indicator answers are compatible with shared evaluation!
For q 1 , q 2 , q 1 is a sub-query of q 2 , we have 𝐼𝐴(𝑞 1 )⊇𝐼𝐴( 𝑞 2 ).
Popularity Order
Query (b)
Query (c)
B. Obama
V. Putin
M. Botvinnik
G. Kasparov
P. Morphy
E. Lasker
Rank: 4
Rank: 1
31/12/2018
ICDE 2016, Helsinki, Finland

10. Refine Optimization 3: Partial Evaluation
Observation 3: Even 𝐼𝐴 𝑞 needs not be completely obtained to reject 𝑞.
Only a lower bound of 𝐼𝐴 𝑞 is needed.
Instead of one list 𝐼𝐴 𝑞 , we keep two: nodes confirmed/uncertain to be in 𝐼𝐴 𝑞 .
Reject 𝑞 immediately if the confirmed list is long enough (>𝑘−|𝐼|).
The number of “match” checks can be reduced.
Popularity Order
Query (b)
Query (c)
B. Obama
V. Putin
M. Botvinnik
G. Kasparov
P. Morphy
E. Lasker
Rank: 4
Rank: 1
31/12/2018
ICDE 2016, Helsinki, Finland

11. Experimental Settings
Datasets
Knowledge base: DBpedia 3.9. Popularity ranking: PageRank score.
Queries: 52 questions from 250 in the QALD-4-Task-1 dataset
Allocated into 5 groups w.r.t. the shape (size, radius) of their ground truth query.
Compared variants:
RkNPQ-gStore: Trivial refine using gStore [Zou, PVLDB’11]
RkNPQ-S: Shared evaluation
RkNPQ-SI: Shared evaluation of Indicator answers
RkNPQ-SPI: Shared and Partial evaluation of Indicator answers
Methodology and Metrics:
Use top-1/2 results to call our algorithms;
Investigate the effectiveness (# returned queries) and efficiency (running time).
31/12/2018
ICDE 2016, Helsinki, Finland

12. Effectiveness
Classify questions into Easy/Moderate/Hard w.r.t. # returned queries.
Simpler question groups have more Easy/Moderate questions.
More example answers cause many questions to turn Easy/Moderate.
The inherent ambiguity of the input is reduced.
Two examples are generally enough for a browsable output.
31/12/2018
ICDE 2016, Helsinki, Finland

13. Efficiency Compare adjacent pairs of the four variants.
When 𝐼 =𝑘=1, the three optimizations speed up RkNPQ by one to two orders of magnitudes, respectively.
When 𝐼 =𝑘=2, 𝐼𝐴 𝑞 →𝐷(𝑞), making indicator answers less beneficial.
31/12/2018
ICDE 2016, Helsinki, Finland

14. Analysis of Parameter k
What happens if we fix 𝐼 =1 and increase k?
# Returned queries? Running time (RkNPQ-SPI)?
More queries are returned.
As k increases, the number tends to converge.
Larger k prevents unsuccessful search, but hardens the browse of returned queries.
RkNPQ-SPI is almost always faster than RkNPQ-SI.
The running time increases slowly.
31/12/2018
ICDE 2016, Helsinki, Finland

15. Related Work Reverse Engineering Structured Queries
SQL queries: [Tran, SIGMOD’09] and [Zhang, SIGMOD’13]
Interactive setting: [Bonifati, EDBT’14], [Starworko, ICDT’12], and [Bonifati, EDBT’15]
Reverse Query Problems for Vector Data
Reverse top-k queries: [Vlachou, ICDE’10]
Reverse KNN queries: [Korn, SIGMOD’00]
Reverse skyline queries: [Dellis, PVLDB’07]
Query by Example Entities & Tuples
[Jayaram, TKDE’15], [Lim, EDBT’13], and [Mottin, PVLDB’14]
Natural Language QA over Knowledge Bases
[Unger, WWW’12], [Yahya, EMNLP’12], [Berant, EMNLP’13], and [Zou, SIGMOD’14].
31/12/2018
ICDE 2016, Helsinki, Finland

16. Conclusions
We propose Reverse top-k Neighborhood Pattern Queries to help users issue knowledge base queries using representative partial answers.
The search space is explored under a filter-refine framework.
Three optimizations on the refine stage are investigated.
Shared evaluation, indicator answers, and partial evaluation.
(When given enough examples) the RkNPQ-SPI algorithm can generate a small number of possible queries for the user within reasonable time.
31/12/2018
ICDE 2016, Helsinki, Finland

17. Thank you!
Questions?
31/12/2018
ICDE 2016, Helsinki, Finland