1. CoXML: A Cooperative XML Query Answering System
Shaorong Liu and Wesley W. Chu
APWeb/WAIM 2007
12/6/2019

2. Motivation
XML has become the standard format for information representation and data exchange
XML schema is usually very complex
E.g., the XML schema for the IEEE Computer Society publications contains about 170 distinct tags and more than 1000 distinct paths
It is often unrealistic for users to fully understand a schema before asking queries
Exact query answering is inadequate and approximate query answering is more desirable!
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3. Our Contribution: CoXML
Query
Approximate Answers
Cooperative XML
Query Answering
XML Database
Engine
XML Documents
A new paradigm for XML approximate query answering that places users and their demands in the center of the design approach
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4. Roadmap Introduction Background CoXML Related Work Conclusion
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5. XML Query Relaxation Types
Value relaxation: enlarging a value condition’s search scope
Node relabel: changing the label a node to a similar or a more general label by domain knowledge
article
title
year
search engine
2003
section
spam detection
article
title
year
search engine
2003
section
spam detection
document
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[1] Tree Pattern Relaxation (S. Amer-Yahia, et al., 2000)

6. XML Query Relaxation Types
Edge generalization: relaxing a ‘/’ edge to a ‘//’ edge
Node deletion: dropping a node from a query tree
article
title
year
search engine
2003
section
spam detection
article
title
year
search engine
2003
section
spam detection
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7. XML Relaxation Properties
Definition
Relaxation operation: an application of a relaxation type to a specific query node or edge
Lemma
Given a query tree with n applicable relaxation operations, there are potentially up to 2n relaxed trees
Possible combinations:
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8. XML Query Relaxation Challenges
Query relaxation is often user-specific
Different users may have different approximate matching specifications for a given query tree
How to provide user-specific approximate query answering?
A query with n relaxation operations has potentially up to 2n relaxed queries
How to systematically relax a query?
Query relaxation generates a set of approximate answers
How to effectively rank the returned approximate answers?
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9. CoXML System Overview Ranking Module Relaxation Engine Relaxation
relaxation language
ranked results
Relaxation Index
query
Ranking
Module
Relaxation
Engine
results
relaxed query
Relaxation
Index Builder
query
exact answers
CoXML
XML
Database Engine
XML Documents
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10. Roadmap Introduction Background CoXML Related Work Conclusion
Relaxation Language
Relaxation Index Structure
Ranking of Approximate Answers
Experimental Studies
Related Work
Conclusion
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11. Relaxation Language A relaxation-enabled query is a tuple {T, R, C, S}
T: tree-pattern query
R: relaxation constructs
E.g., delete/re-label a node, generalize an edge
C: relaxation controls
E.g., prefer/reject certain relaxation operations, use certain relaxation types, control relaxation orders, etc
S: stop condition
E.g., the minimum # of approximate answers to be returned
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12. Relaxation Language Example
<inex_topic topic_id="267" >
<castitle>
//article//fm//atl[about(., "digital libraries")]
</castitle>
<description>
Articles containing "digital libraries" in their title.
</description>
<narrative>
I'm interested in articles discussing Digital Libraries as their main subject. Therefore I require that the title of any relevant article mentions "digital library" explicitly. Documents that mention digital libraries only under the bibliography are not relevant, as well as documents that do not have the phrase "digital library" in their title.
</narrative>
</inex_topic>
article fm
atl
“digital libraries” $1 $2 $3
C = !Rel($3, -)  !Del($3)
 Reject($2, bb)
!Rel($3, -) : $3 cannot be re-labeled
!Del($3): $3 cannot be deleted
Reject($2, bb): $2 cannot be re-labeled to bb
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13. How to Relax Queries? Naïve approach Observation
Generate all possible relaxed queries & iteratively select the best relaxed query to derive approximate answers
Exhaustive, but not scalable
Observation
Many queries share the same (or similar) tree structures
Our approach: relaxation index structure
Consider the structure of a query tree T as a template
Build indexes on the relaxed trees of T
Use the index to guide the relaxations of any query with the same (or similar) tree structure as that of T
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14. Relaxation Index Structure - XTAH
A hierarchical multi-level labeled cluster of relaxed trees for a given query tree
Building an XTAH
Given a query structure template T, generate all possible relaxed trees
Each relaxed trees uses an unique set of relaxation operations
Cluster relaxed trees into groups based on relaxation operations and distances -- similar to “suffix-tree” clustering
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15. XTAH Example for Template Structure T
{gen(e$1,$2)} …
{gen(e$3, $4)}
{del($2)}
node_relabel
edge_generalization
node_deletion
relax
{gen(e$3, $4), gen(e$1,$3)}
...
article
body
section T6
{gen(e$1, $2), gen(e$3, $4)}
{del($2), del($3)}
title T2 T4 T3 T1 T7
article
title
body
section $1 $2 $3 $4
Template structure T
gen(e$u, $v) – relaxing the edge between $u and $v
del($u) – deleting the node $u
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16. XTAH Properties
Each group consists of a set of relaxed trees derived from similar relaxation operations
The relaxed trees can be located efficiently based on the type of relaxation operation
The higher level group in the XTAH yields lesser relaxation than the lower group
Query can be relaxed to different level of granularities by traversing up and down the XTAH
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17. Ranking of XML Approximate Answers
Content similarity – cont_sim(A, Q)
An extended vector space model [2]
Structure similarity – struct_dist(A, Q)
Use tree editing distance for measuring structure similarity
Propose a cost model that assigns operation cost based on relaxation semantics
Overall relevancy – sim(A, Q)
A ranking model combing both content similarity and structure distance
 is a small constant between 0 and 1
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[2] Configurable Indexing and Ranking for XML Information Retrieval (S. Liu, et al., 2004)

18. Experimental Studies Experiment Setup Evaluation Metrics
INEX (INitiative for the Evaluation of Xml) 05 test collection
Document collection
Query set
Gold standard
Evaluation Metrics
nxCG (normalized extended cumulative gain)
the official evaluation metric used in INEX 05
Given a number i (i1), similar to measures the relative gain users accumulated up to the rank i
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19. Retrieval performance improvements with semantic cost model
Query set: all content-and-structure queries in INEX 05
(, cost model) 
Cost Model
0.1
0.3
0.5
0.7
0.9
Uniform
0.2584
0.2616
0.2828
0.2894
0.2916
Semantic
(+28.44%)
(+21.94%)
(+13.04%)
(+6%)
(+4.08%)
Assigning relaxation operation with different cost based on the similarities of the nodes being operated improves retrieval performance!
and yield similar results
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20. Evaluation of Relaxation Control
article fm
atl
“digital libraries” $1 $2 $3
C = !Rel($3, -)  !Del($3)
 Reject($2, bb)
Query: topic 267
Result:
Evaluation Metric
Method
No relaxation control
0.1013
0.2365
With relaxation control
1.0
0.8986
Perfect accuracy
Relaxation control enables the system to provide answers with greater relevancy!
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21. Related Work
Relaxation based on schema conversions ([LC01, LMC01], [LMC03])
Without structure relaxation
Native XML relaxation
Proposed structure relaxation types [e.g., KS01, ACS02]
Used the relaxation types [ACS02] in our work
Investigate efficient algorithms for deriving top-K answers based on relaxation types [e.g, Sch02, ACS02, ALP04, AKM05]
Without relaxation control
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22. Conclusion Cooperative XML (CoXML) query answering
Relaxation-enabled query language allows users to effectively express the relaxed query conditions as well as controlling the relaxation process
XTAH provides systematic query relaxation guidance
Used both content and structure similarity metrics for evaluating the relevancy of approximate answers
Evaluation studies with the INEX test collections validate the effectiveness of our methodology
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