milan zdravkovic, miroslav trajanovic, hervé panetto, local ontologies for semantic...
DESCRIPTION
13th International Conference on Enterprise Information Systems, ICEIS’2011, June 8-11, 2011, Beijing, P.R. ChinaTRANSCRIPT
1
Local Ontologies for Semantic Interoperability in Supply Chain Networks
Milan Zdravković, Miroslav Trajanović
University of Niš, Serbia
[email protected], [email protected]
Hervé Panetto
Research Centre for Automatic Control (CRAN – UMR 7039), Nancy-Université, CNRS, France
ICEIS’2011, June 8-11, 2011, Beijing, P.R. China
Problems of “traditional” supply chains
• High-speed, low-cost
Cost reduction is a key aspect of collaboration• Reduced number of suppliers• Dyadic relationships management• High level of integration
• High costs
• Reduced flexibility
Often, SC can’t respond effectively to structural
changes in demand
*Virtual BreedingEnvironment
Virtual organizations – Supply chains of the future ?
Ent2
Ent4
Ent1
Ent3
Ent5Ent6
**Virtual Enterprise 1
Ent21
Ent41
Ent11
Ent31
Ent61
**Virtual Enterprise n
Ent2n
Ent4n
Ent5n
Ent3n
Opportunity 1 Opportunity n
Sel
ecti
onC
onfi
gura
tion
Sel
ecti
onC
onfi
gura
tion
Dis
solu
tion
Dis
solu
tion
**Temporary network of independent enterprises, who join together quickly to exploit fast-changing opportunities and then dissolve (Browne and Zhang, 1999)
* Pool of organizations and related supporting institutions that have both the potential and the will to cooperate with each other through the establishment of a “base” long-term cooperation agreement and interoperable infrastructure. (Sánchez et al, 2005)
What is interoperability ?
• ISO/IEC 2382• 01.01.47 interoperability: The capability to communicate, execute
programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units.
• The main prerequisite for achievement of interoperability of the loosely coupled systems is to maximize the amount of semantics which can be utilized and make it increasingly explicit (Obrst, 2003)
Is it easy ?
English translation of Welsh: “I am not in the office at the moment. Please send
any work to be translated”
What is Semantic Interoperability ?
• system(S) system(R) semantically-∧ ∧interoperable(S,R) ⇒
• ∀p (• (transmitted-from(p,S) transmitted-to(p,R)) q(statement-∧ ∧ ∀
of(q,S) p q) q’(statement-of(q’,R) p q’ q’⇔q)∧ ⇒ ∃ ∧ ⇒ ∧• )
Cn
C1
C2
Implementation of semantically interoperable systems
OL1
OD1
OL2
ML1D1
ML2D1
MO1O2≡f(ML1D1 , ML2D1)
S1
S2
MLnD1
Sn
OLn
MO1On≡f(ML1D1 , MLnD1)
OD2
Si
OLi
MLiD2
MD1D2
MO1Oi≡f(ML1D1 , MD1D2, MLiD2)
• S1-Sn – Enterprise Information Systems
• OL1-OL2 – Local ontologies
• OD1,2 – Domain ontologies
• MLiDi – Mappings between local and domain ontologies
Our approach to semantic interoperability in supply chain networks 1/2
• Based on Supply Chain Operations Reference (SCOR)• standard approach for analysis, design and implementation of five core
processes in supply chains: plan, source, make, deliver and return• it defines a framework, which aims at integrating business processes,
metrics, best practices and technologies with the objective to improve collaboration between partners
• SCOR model is implicit• It is semantically enriched (SCOR-Full), when common general
properties are recognized and used to aggregate the SCOR concepts into general notions
• Approach reduces the development time, as it builds upon the existing consensus of the domain experts, transposed into the SCOR reference model
Our approach to semantic interoperability in supply chain networks 2/2
SCOR-FULL OWL
SCOR-SYS OWL
SCOR-KOS OWL
SCOR Native formats, Exchange formats
DomainOntologies
Implicit semantics Explicit semantics Semantic enrichment
Formal models of design goals
Semantic applications
Enterprise Information
Systems
SCOR-based systems
SCOR-CFG OWL
SCOR-GOAL OWL
PRODUCT OWL
SemanticQuery service
EIS database
LOCAL ONTOLOGY
Transformationservice
EIS database
LOCAL ONTOLOGY
EIS database
LOCAL ONTOLOGY
SC
OR
- M
AP
Where is enterprise semantics ?
• Our assumptions in this approach:• Enterprise realities are represented by the
corresponding enterprise information systems (EIS).• Enterprise message models are based on EISs’ data
models, represented implicitly in their databases.
• Semantics of the business logic (from EISs) remains hidden• with exception of database triggers, unless they are
used to enforce referential integrity
Database
er.owl
attribute
constraint
entity
multiplicityrelatio
n
type
hasAttribute
hasType
hasConstraint
hasSourceAttribute
hasDestinationAttribute
hasSourceMultiplicity
hasDestinationMultiplicity
output
imports
s-er.owl
concept hasObjectProperty
data-type
hasDataProperty data-concept
hasDataType
hasDefiningProperty
hasDefiningDataPropertyhasFunctionalProperty
output
er:entity(x) not (er:hasAttribute only ∧(er:attribute (er:isSourceAttributeOf some ∧er:relation))) ⇒ s-er:concept(x)
er:entity(x) er:entity(y) er:relation(r) ∧ ∧ ∧er:hasAttribute(x, a1) er:hasAttribute(y, ∧a2) er:isDestinationAttributeOf(a2, r) ∧ ∧er:isSourceAttributeOf(a1, r) ⇒s-er:hasObjectProperty(x, y)
s-er:hasObjectProperty(x, y) ∧er:hasConstraint(a1,'not-null') ⇒s-er:hasDefiningProperty(x, y)
er:attribute and not (er:isSourceAttributeOf some er:relation) ⇒ s-er:data-concept
er:type(x) ⇒ s-er:data-type(x)s-er:concept(c) er:attribute(a) er:type(t) ∧ ∧
er:hasAttribute(c, a) er:hasType(a, t) ∧ ∧ ⇒s-er:hasDataProperty(c, t)
s-er:hasDataProperty(c, t) ∧er:hasConstraint(a,'not-null') ∧er:hasConstraint(a,'unique') ⇒s-er:hasDefiningDataProperty(c, t)
Our approach to database-to-ontology mapping
Data import andclassification of ER entities
Classification (inference) of OWL types and properties
LexicalRefinement
Local ontologygeneration
output
SCOR-MAP
DOMAINONTOLOGY1
Transform F1-Fn to common format and merge to F
USE1
USE2
USEn
F1
F2
Fn
DLQD1
ST
Extraction of data from heterogeneous sources
Merge RS1-RSn to RS
EIS database
EIS database
EIS database
SQLQ1
SQLQ2
SQLQn
RS1
RS2
RSn
ST≡ ST1U ST2U ST3
LOCALONTOLOGY
LOCALONTOLOGY
LOCALONTOLOGY
DLQ1
DLQ2
DLQn
ST1
ST2
STn
DOMAINONTOLOGY2
DOMAINONTOLOGYm
DLQD2,..,DLQDm
• Use EISs (USEi) to export data files (Fi) then transform and merge
• Execute SQL queries (SQLQi) to get result sets (RSi) then merge
• Execute DL queries (DLQi) to get sets of triples STi where resulting set is their union
• Use dictionary to extract data with a single DL query
• Use other dictionaries to extract the same data
Semantic query
• A pair (O,C)• O – set of concepts to be inferred• C – set of restrictions to be applied on their properties
• Only restrictions can be used in queries, because concepts are inferred as property domains and ranges.• e.g. hasAccountAccountType some (hasCode value 3)• However, in case of very general concepts, such as “name” or “id”
this may be troublesome (or not ?)
• Domain of hasName property in openERP local ontology is union of 170 concepts
• But, you don’t have to know anything about underlying ER model in order to launch queries
Semantic query executionInput Query
hasResCompany some(hasResCurrency some
(hasName value "EUR"))
Decomposition
subject predicate some|only|min n|max m|exactly o bNodesubject predicate value {type}
XhasResCompany
some bNode1
bNode1hasResCurrency
some bNode2
bNode2hasName
value "EUR"
SQL constructand execute
bNode2 nothing ?
bNode1 nothing ?
X nothing ?
Assert totemporary mdl
SQL constructand execute
No
Assert totemporary mdl
SQL constructand execute
No Yes
Yes
Assert totemporary mdl
No
Temp mdl isresulting mdl
No result
Yes
• Each SQL query returns data which is used to generate OWL statements which are asserted to a temporary model
• Temporary model is a graph, which focal concept is bNode2 (first step), bNode1 (second step) and X (final step)
• End result is a graph, which focal concept is X
Conclusions
• Widely adopted supply chain process reference model is used as a starting point for semantic interoperability framework• Weak consequences of “inspirational approach” avoided• Consensus on the model already exists in community• Development reduced – you don’t have to start from the scratch
• Enterprises are “introduced” to interoperable world in supply chain networks• With their partial realities (EISs databases), explicitly represented
(corresponded to a “common knowledge”)• Did we just described two Interoperability Service Utilities (ISU) –
Transformation Service and “Ask” interface of Semantic Query Service ?
Gaps and future challenges
• Business logic is not explicated, its difficult to find it even in implicit form
• Semantics of the database data is not analyzed (e.g. based on data occurrence patterns)
• Local ontology need to be enacted – in our approach it is considered only as an intermediary model
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Local Ontologies for Semantic Interoperability in Supply Chain Networks
Milan Zdravković, Miroslav Trajanović
University of Niš, Serbia
[email protected], [email protected]
Hervé Panetto
Research Centre for Automatic Control (CRAN – UMR 7039), Nancy-Université, CNRS, France
ICEIS’2011, June 8-11, 2011, Beijing, P.R. China
Thank you for your attention