Matching and Reuse of XML Schemas
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Sample XML Schema
<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
<xs:element name="car">
<xs:complexType>
<xs:sequence>
<xs:element name="make" type="xs:string"/>
<xs:element name="model" type="xs:string"/>
<xs:element name="year" type="xs:string"/>
<xs:element name="color" type="xs:string"/>
<xs:element name="driver">
<xs:complexType>
<xs:sequence>
<xs:element name="first" type="xs:string"/>
<xs:element name="last" type="xs:string"/>
<xs:element name="license" type="xs:string"/>
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:schema>
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What is XML schema matching
 Matching – identifying the relations among the
corresponding elements of two schemas
 e.g. customer/firstName <==> client/name/first
customer/name <==>
concatenate (client/name/first, client/name/last)
 Calculate the distance between two Schemas
 E.g., distance between customer.xsd and client.xsd is 0.67.
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Why XML Schema matching
 From data integration point of view:
 Purpose: Automatically identifying corresponding elements between two
schemas
 Relevant works:
 Database schema matching/mapping, e.g., A. Doan, et al., Reconciling schemas of
disparate data sources: A machine-learning approach. SIGMOD, 2001
 Generic schema mapping, e.g., J. Madhavan, P. A. Bernstein, E. Rahm. Generic schema
matching with Cupid. VLDB, 2001.
 XML Schema matching. E.g. H. Do, E. Rahm. COMA A system for flexible combination of
schema matching approaches. VLDB 2002.
 From web service composition point of view
 e.g., matching the output type of one service with the input of another in
sequential composition
 From software reuse point of view:
 Purpose: Build XML Schema categories and search engines;
 Relevant works:
 Software component search: A Mili, R Mili, RT Mittermeir, A survey of software reuse
libraries, Annals of Software Engineering, 1998.
 Agent and service matching: Katia Sycara, Jianguo Lu, Matthias Klusch, Interoperability
among Heterogeneous Software Agents on the Internet, Technical Report CMU-RI-TR4
98-22, CMU.
What are the problems
 Modelling
 As graph
 As tree matching
 Node similarity
 Name, type, cardinality.
 Structure similarity
 Tree edit distance
 K. Zhang, D. Shasha. Simple fast algorithms for the editing distance
between trees and related problems. SIAM Journal of Computing, 1989.
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Overview of our system
Modelling
Node Relations
Structural Relations
Name Relations
XML
Schema
XML
Schema
Name
Similarity
Node
Similarity
Structural
similarity
Results
retrieval
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Three similarities
Name
Similarity
Node name
Node
Similarity
User-defined
data type
WordNet,
string matching
Hungarian method
Built-in
data type
Structural
Similarity
Cardinality
Compatibility
tables
Hierarchical
structure
Tree matching
algorithm
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Modelling
Model schemas as trees
<xs:element name="driver" type="driverType"/>
<xs:attribute name="license" type="xs:string"/>
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Modelling
schema
customerOrder
paper
shipping
address
billing
Model schemas as trees
reference
title author contents
date ship2Add
refNo
Reference
schema
Address_ca.xsd
customerOrder
Address_us.xsd
address
address
billing
street
date ship2Add
street province postcode
paper
Recursion
shipping
date bill2Add
province postcode
street
date bill2Add
Importing and Inclusion
state
zip
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Information excluded in Modelling
 Related to elements or attributes
Model schemas as trees
 Default value, value range, unique, nullable…
 Related to structure
 Sequence
 All
 Choice
name
first last
name
last
first
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Computing node similarity
 Computing name similarity with the help of:
Node similarity
 WordNet and its API
 String matching
 Hungarian method
 Add the similarity of other information
 Data type
 Minimum cardinality
 Maximum cardinality
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Name similarity from token lists
 Tokenize names
 E.g. clientName -> client name
submittedReports -> submit report
Node similarity
 Similarity between two token lists
 Using Hungarian method for Weighted Bipartite Graph Matching
(WBGM)
customerDeliveryAddress
customer
vs.
clientRequiredShippingAddress
client
sim0,0
require
delivery
shipping
address
simi,j
address
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Determine the structural relation
Structure similarity
Tree 1
Tree 2
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Common substructure
Structure similarity
make
firstName
model
lastName
year
car
license
driver
color
make
model
first
car
driver
last
year
license
color
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Approximate Common Structure
Structure similarity
make
firstName
model
lastName
year
car
license
driver
color
make
model
first
car
driver
last
year
license
color
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Mappings in an ACS
Structure similarity
make
ACS1
model
year
car
ACS2
color
first (firstName)
driver
mACS1 = {(s1.car, s2.car),
(s1.make, s2.make),
(s1.year, s2.year),
(s1.color, s2.color)}
last (lastName)
mACS2 = {(s1.dirver, s2.driver),
(s1.fist, s2.firstName),
(s1.last, s2.lastName),
(s1.license, s2.license)}
license
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Evaluation
 Criteria
 Matching outcomes
 Mappings
 Schema similarity
Evaluation
 Execution time
 Collected four groups of Schemas




Purchase orders used in COMA (5)
Large schemas from XML.org (86)
Schemas on hospitality domain (95)
Extract from WSDL (419)
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Comparison with edit distance algorithm element
mapping on data group 1
Precision by method 1
Precision by method 2
Recall by method 1
Recall by method 2
Av
g
&5
5
Ta
sk 1
04
3&
4
Ta
sk 9
3&
5
Ta
sk 8
2&
4
Ta
sk 7
2&
3
Ta
sk 6
2&
5
Ta
sk 5
1&
4
Ta
sk 4
1&
3
Ta
sk 3
1&
2
Ta
sk 2
1&
Ta
sk 1
Evaluation
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Method 1: our algorithm
Method 2: edit distance
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Comparison with edit distance: schema similarity data
group 3 and 4
Top-k Precision
1.0
Evaluation
0.8
Top-3 Precision
0.6
Top-5 Precision
0.4
0.2
0.0
Method 1 on
Method 1 on
Method 2 on
Method 2 on
Schema group Schema group Schema group Schema group
3
4
3
4
Method 1: our algorithm
Method 2: edit distance
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Comparison with edit distance: performance
on data group 2
Avg Matching Time 1
Avg Matching Time 2
250
150
100
50
-2
0k
16
k
-1
6k
14
k
-1
4k
12
k
-1
2k
10
k
10
k
8k
-
8k
6k
-
6k
4k
-
4k
2k
-
2k
1k
-
1k
0
0-
(seconds)
Evaluation
200
Input size (M*N)
Method 1: our algorithm
Method 2: edit distance
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Comparison with COMA (Mapping)
COMA – 'All'
COMA – 'All+SchemaM'
Our algorithm
Evaluation
Precision
about 0.95
about 0.93
0.88
Recall
about 0.78
about 0.89
0.87
0.73
0.82
0.75
Overall
Overall is a measure that combines precision and recall. It
reflects the efforts of removing incorrect mappings and adding
missing ones.
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Conclusion
 Scalable schema matching
 Wang Lian, David W. Cheung, Nikos Mamoulis, and Siu-Ming Yiu,
An Efficient and Scalable Algorithm for Clustering XML Documents
by Structure, TKDE, 2005.
 Subtyping
 Apply to web service matching
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Web service synthesis
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Web Service Composition
composition
 Composite web service: “service implemented by
combining the functionality provided by other web
services” –G. Alonso et al.
 Web service composition: the process of developing a
composite web service
 Approaches to web service composition:




Conventional programming languages, such as Java, C#;
Web service composition languages, such as BPEL;
Workflow, pi-calculus, petri net, automata…
Web service synthesis.
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Web Service Synthesis
 BPEL and the like are still programming languages
 They describe exactly how to compose the web services.
 Web service synthesis
composition
 We describe what is the service. But don’t describe how to
implement it;
 We don’t even know what are the component services involved;
 The relevant services are discovered and invoked dynamically;
 The implementation is synthesized from the web service
specification, automatically.
 Program synthesis has a long history.
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Web Service Synthesis
WS
Syntactic Specification (WSDL)
Semantic Specification (Datalog)
composition
WS2
WS1
Service Specification (WSDL/Datalog)
Service Implementation
WS
Service Implementation (BPEL)
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Synthesis Example
Chapters
MetaSearchService
Service specification
Syntactic:
Interface definition defined by WSDL
composition
Semantic:
Q(ISBN, PRICE, TITLE, RATE) <Chapters(ISBN, PRICE),
Book1(TITLE, ISBN, AUTHOR),
Book2(ISBN, COMMENT, RATE).
Syntactic specification: …
Semantic Specification:
chapters(ISBN, PRICE, TITLE, AUTHOR) <Chapters(ISBN, PRICE), Book1(TITLE, ISBN,
AUTHOR).
amazon
Service Specification
Syntactic specification:
WSDL file
MetaSearchService
Implementation
??
Semantic Specification:
amazon(ISBN, PRICE, RATE, TITLE, AUTHOR) <Amazon(ISBN, PRICE),
Book1(TITLE, ISBN, AUTHOR),
Book2(ISBN, COMMENT, RATE).
Service Implementation
Java code, database
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Generate the abstract implementation by query rewriting
MetaSearchService
Service specification
Syntactic:
Interface definition defined by WSDL
composition
Semantic:
Q(ISBN, PRICE, TITLE, RATE) <Chapters(ISBN, PRICE),
Book1(TITLE, ISBN, AUTHOR),
Book2(ISBN, COMMENT, RATE).
Chapters
Syntactic specification: …
Semantic Specification:
chapters(ISBN, PRICE, TITLE, AUTHOR) <Chapters(ISBN, PRICE), Book1(TITLE, ISBN,
AUTHOR).
amazon
Service Specification
Syntactic specification:
WSDL file
MetaSearchService Abstract
Implementation
Q(ISBN, PRICE, TITLE, RATE) <amazon(ISBN, PRICE, RATE, TITLE', AUTHOR'),
chapters(ISBN, PRICE0, TITLE, AUTHOR).
Semantic Specification:
amazon(ISBN, PRICE, RATE, TITLE, AUTHOR) <Amazon(ISBN, PRICE),
Book1(TITLE, ISBN, AUTHOR),
Book2(ISBN, COMMENT, RATE).
Service Implementation
Java code, database
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Generate the Concrete Implementation
MetaSearchService
Service specification
Syntactic:
Interface definition defined by WSDL
Semantic:
Q(ISBN, PRICE, PRICE0, TITLE, RATE) <…
Chapters
Syntactic specification: …
Semantic Specification:
chapters(ISBN, PRICE, TITLE, AUTHOR) <Chapters(ISBN, PRICE), Book1(TITLE, ISBN,
AUTHOR).
composition
amazon
Service Specification
MetaSearchService Abstract
Implementation
Q(ISBN, PRICE, PRICE0, TITLE, RATE) <amazon(ISBN, PRICE, RATE, TITLE', AUTHOR'),
chapters(ISBN, PRICE0, TITLE, AUTHOR).
MetaSearchService Concrete
Implementation
Invoke amazon;
Invoke chapters;
Combine the output;
Syntactic specification:
WSDL file
Semantic Specification:
amazon(ISBN, PRICE, RATE, TITLE, AUTHOR) <Amazon(ISBN, PRICE),
Book1(TITLE, ISBN, AUTHOR),
Book2(ISBN, COMMENT, RATE).
Service Implementation
Java code, database
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It is a lightweight approach…
composition
 Web services are restricted to be database queries or
functions that can be described by database queries or
Datalog;
 Semantic specification is Datalog instead of more powerful
specification mechanism employing ontology;
 Compositions are restricted to data composition instead of
full-blown process specification such as BPEL.
 All those choices are meant for the construction of a
practical web service synthesis system…
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Mapping between Datalog and Web Services
 Database vendors also provide wrappers for web services
composition
 Behind a web service there is a SQL query that corresponds to the
web service;
 SQL defines the semantics of the web service.
 Major database vendors support the mapping between SQL and
Web service;
 We experimented with DB2WS.
Malaika, S. et al. DB2 and Web Services. IBM System Journal, 41(4), pp. 666685. 2002.
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Generate the Abstract Implementation by Query
rewriting
Definition: Given a query Q and a set of views V. A
rewriting of Q using V is a query Q’ such that Q=Q’,
and Q’ refers to one or more views in V.
composition
Views:
V1T1,T2.
V2T2,T3.
Query:
Q  T1, T2, T3.
Rewriting 1:
Q V1, T3.
Rewriting 2:
Q  V1, V2.
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Our query rewriting system
composition
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Limitations of our approach
 Focus on database web services;
 Datalog is not expressive enough.
 Query rewriting in Description Logic, or OWL.
composition
 Assume the existence of global database schemas:
 Service providers need to provide the semantic definition of web
services in terms a global database schema;
 New service specification is also defined using the common schema
 Schema matching
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Other threads
 Web service collection and clustering
 From UDDI, Crawler, Search engines such as Google
 Master thesis to be finished this summer
 Web service metrics
 Schema subtyping
 Based on regular tree grammar
 Master thesis to be finished this summer
 Bottom up web service composition
 Semantic web service
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Service Oriented Architecture
Discovery
agency
publish
find
Requester
interact
Provider
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Web service discovery
 Keywords search
 Based on IR techniques, such as vector space model
 Fast, but not accurate
 Signature matching
 Decide subtype relations between input and output of web services
 Used in service composition, to find composable web services
 Relaxed matching
 Approximate matching, allowing small deviations in both structure
and words/tags
 Semantic matching
 Matching functional requirements of web services
 Used in adaptive, autonomous systems
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