iSURF An Interoperability Service Utility for Collaborative Supply Chain Planning across Multiple Do

1
iSURF -An Interoperability Service Utility for
Collaborative Supply Chain Planning across
Multiple Domains
METU OASIS SET TC Use Case

• Prof. Dr. Asuman Dogac
• METU-SRDC
• Turkey

2
METU OASIS SET TC Use Case

• Part I iSURF -An Interoperability Service
  Utility for Collaborative Supply Chain Planning
  across Multiple Domains and the Document
  Interoperability Requirements of iSURF
  Interoperability Service Utility
• Part II Using SET Tools for translating iSURF
  Planning Documents Conforming to Different
  Standards

3
Part I iSURF -An Interoperability Service
Utility for Collaborative Supply Chain Planning
across Multiple Domains and the Document
Interoperability Requirements of iSURF
Interoperability Service Utility
4
Research Objectives Public Domain Tools
Supporting SMEs for Collaborative Supply Chain
Planning

• iSURF open Smart product Infrastructure for SMEs
  to collect real-time supply chain visibility data
  
• iSURF Service Oriented Collaborative Supply Chain
  Planning Process Definition and Execution
  Platform for the SMEs
• iSURF Semantic Interoperability Service Utility
• iSURF Global Data Synchronization and Transitory
  Collaboration Service Utility for dynamic
  transient supply chain relationships for the SMEs

5
iSURF Overview
6
Part II Using SET Tools for translating iSURF
Planning Documents Conforming to Different CCTS
based Standards
7
The Main Ideas of the SET Framework

• If the document components of two different CCTS
  based standard share the same semantic
  properties
• Use this as an indication that they may be
  similar
• Some explicitly defined semantic properties may
  imply further implicit semantic relationships
• Use a reasoner to obtain implicit relationships
• Explicate semantics related with the different
  usages of document data types in different
  document schemas to obtain some desired
  interpretations by means of such informal
  semantics
• For discovering the similarities of structurally
  different but semantically similar document
  artifacts, we provide further heuristics
• About possible ways of organizing core components
  into compound artifacts

8
Semantic Properties of UN/CEFACT CCTS based
Standards

• The Core Components have the following semantic
  properties
• Core Component Data Types
• Context
• Code Lists
• Object Class Term
• Representation Term
• The semantics that a BIE is based on a Core
  Component

9
The Upper Ontology for the Semantics Exposed by
the CCTS Framework
10
Upper Ontologies of Some of the CCTS based
Standards and their Relationships to CCTS Ontology
11
The current SET Harmonized Ontology

• The current version of the harmonized ontology
  contains the ontological representations of
• All of the CCs and BIEs in CCL 07B
• All of the BIEs in the common library of UBL 2.0
• All of the OAGIS 9.1 Common Components and Fields
• All of the elements in the common library of GS1
  XML
• There are about 4758 Named OWL Classes and 16122
  Restriction Definitions in the current version of
  the harmonized ontology

12
Upper Ontologies and their Relationship to the
Document Schema Ontologies
13
A Specific Instance of the Problem

• How to transform
• UBL 2.0 Forecast Instance, to
• GS1 XML Forecast Instance?

14
(No Transcript)
15
The first step

• Convert the XSDs of these document instances to
  OWL conforming to SET specifications
• SET XSD-OWL Converter tool can be used to
  generate the OWL definitions of the XSDs
  conforming to SET Specifications
• http//www.srdc.metu.edu.tr/iSURF/OASIS-SET-TC/too
  ls/OASISSET.zip

16
OWL Definition of UBL Forecast Document
17
OWL Definition of GS1 XML Forecast Document
18
Explicate semantics related with the different
usages of document data types

• Different document standards use CCTS Data Types
  differently
• For example, Code.Type" in one standard is
  represented by Text.Type" in another standard
  and yet with Identier.Type" in another standard
• This knowledge in real world is expressed through
  class equivalences so that not only the humans
  but also the reasoner knows about it
• Code.Type Text.Type
• Name.Type Text.Type
• Identier.Type Text.Type

19
The Above equivalences are discovered through the
SET Harmonized Ontology
20
The Above equivalences are discovered through the
SET Harmonized Ontology
21
The Above equivalences are discovered through the
SET Harmonized Ontology
22
Addressing Structural Differences in Document
Schemas

• The harmonized ontology is effective only to
  discover equivalence of both semantically and
  structurally similar document artifacts
• However Different document standards use core
  components in different structures
• A problem in finding the similar artifacts in two
  different document schemas is that the
  semantically similar artifacts may appear at
  structurally different positions
• SET proposes heuristic rules for this

23
Heuristics to Address Structural Differences in
Semantically Equivalent Document Artifacts

• This heuristics is about possible ways of
  organizing core components into compound
  artifacts and are given in terms of predicate
  logic rules
• Note that a DL reasoner by itself cannot process
  predicate logic rules and we resort to a well
  accepted practice of using a rule engine to
  execute the more generic rules and carry the
  results back to the DL reasoner through wrappers
  developed
• The results involve declaring further class
  equivalences in the harmonized ontology

24
A Heuristic to Help Finding the Equivalent BBIEs
at Different Structural Levels

• A BBIE, that directly appears under an ABIE in
  one schema, may be referred through an ASBIE (at
  any depth) in an another document schema
• To give a hint to the reasoner of such
  possibilities, we developed a piece of software
  that automatically asserts a subsumtion hierarchy
  among the Object Class Terms of such document
  artifacts
• More specifically, when an ABIE A1" refers to a
  "BBIE B" in an "ABIE A2" through an "ASBIE AS" in
  one document schema, the Object Class Term of the
  "BBIE B" is made a subclass of "ABIE A1"
• Note that once such an assertion is made, then
  the reasoner can recursively trace the ASBIEs at
  any depth

25
Heuristics to Discover Structurally Different
BBIEs

• A very common structural difference in
  semantically similar document artifacts is that
  although some of the semantic properties of a
  document artifact A is the subclass of the
  corresponding properties of the document artifact
  B, some other properties of A are the super
  classes of the corresponding attributes of B
• Heuristics to Discover Structurally Different
  BBIEs
• If the semantic properties of two BBIEs are pair
  wise equivalent or subclasses of each other,
  these BBIEs are considered to be similar

26
Heuristics to Discover Structurally Different
ASBIEs

• Heuristics to Discover Structurally Different
  ASBIEs
• If the semantic properties of two ASBIEs are pair
  wise equivalent or subclasses of one another, we
  consider these ASBIEs to be equivalent

27
Heuristics to Discover Structurally Different
ASBIE-BBIE Pairs

• Consider two semantically equivalent BBIEs, BBIE1
  and BBIE2
• If BBIE1 is in ABIE1 and ASBIE1 is referring to
  ABIE1, there is a possibility that ASBIE1 is
  semantically equivalent to the BBIE2

28
Heuristics to Discover Structurally Different
ABIEs

• When it comes to ABIEs, the structural
  differences that can occur are more complex
  because each ABIE may contain different number of
  BBIEs some of which may be semantically
  equivalent, some may not
• Therefore while testing whether two ABIEs are
  semantically equivalent, the set of BIEs (the set
  of BBIEs and ASBIEs) they contain is considered
• We define the ContainsSet" of an ABIE to be the
  set of all of its BIEs just to simplify the
  explanation
• The ContainsSet" is in fact the set of BIEs in
  the range of the contains" property of an ABIE
• The ContainsSet"s of two ABIEs may be equal may
  have a nonnul intersection may be in subset
  relationships or may be disjoint of each other
• If the ContainsSet"s are not disjoint, we
  provide heuristics to discover their similarity

29
The ContainsSets of two ABIEs are equivalent or
in subset relationship

• Consider all the semantic properties of two
  ABIEs
• If each of them is pair wise equivalent or
  subclasses of one another, and their
  ContainsSet"s are the same, for our purposes we
  consider these ABIEs to be equivalent

30
The "ContainsSet"s of two ABIEs have a nonnul
intersection

• The semantic properties of two ABIEs may be
  equivalent and their "ContainsSet" may have a
  nonnul intersection
• How to classify these ABIEs is for its user to
  decide
• What we provide is a "similarityConstant" that
  the user may set
• As an example, if the user considers that when
  60 of the BIEs of two ABIEs are the same, they
  may be considered similar, then he can set the
  "similarityConstant" to "0.6"
• When all the semantic properties of two ABIEs are
  either pair wise equivalent or subclasses of one
  another, and the BIEs in their "ContainsSet" sets
  are "similarityConstant" percent equivalent, we
  consider these ABIEs to be similar

31
Example Heuristic Rules
Heuristic Rules help to find the semantically
equivalent but structurally different schemas
32
Methodology
33
SET Framework
R U L E S
Source OWL Instance
Target/Source XSD Document Schemas
Subsumption Relations
Knowledge Base
Rule Engine Reasoner
Source XML Instance
Target XML Instance
XSLT Definition
DATA LEVEL
KNOWLEDGE LEVEL
DATA LEVEL
34
Back to our problem Translating iSURF Planning
Documents Conforming to Different CCTS based
Standards
35
A Specific Instance of the Problem

• How to transform
• UBL 2.0 Forecast Instance, to
• GS1 XML Forecast Instance?

36
(No Transcript)
37
(No Transcript)
38
The Above equivalences are discovered through the
SET Heuristic Rules Provided
39
Transforming UBL Forecast to GS1 XML Forecast

• UBL Forecast document is converted to GS1 XML
  Forecast (and vice versa) through OASIS SET TC
  methodology
• For the Example Planning Documents, SET TC
  Semantic Tools were able to find
• The semantic equivalences of 15 BBIEs out of 22
  BBIEs (68)
• The semantic equivalences of 7 ABIEs out of 15
  ABIEs (46)
• The Information on this slide is WRONG!!!
• System behaves much, much better than this! We
  will provide detailed statistics later!

40
(No Transcript)
41
Generating XSLTs through Altovas MapForce Tool
42
(No Transcript)
43
Thank you for your attention