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Local Ontologies for Semantic Interoperability in Supply Chain Networks Milan Zdravković, Miroslav Trajanović University o...
Problems of “traditional” supply chains <ul><li>High-speed, low-cost </li></ul><ul><li>Cost reduction is a key aspect of c...
Virtual organizations – Supply chains of the future ? *Virtual Breeding Environment Ent 2 Ent 4 Ent 1 Ent 3 Ent 5 Ent 6 **...
What is interoperability ? <ul><li>ISO/IEC 2382 </li></ul><ul><ul><li>01.01.47 interoperability: The capability to communi...
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 ? <ul><li>system(S) ∧ system(R) ∧ semantically-interoperable(S,R) ⇒ </li></ul><ul><li>∀ ...
Implementation of semantically interoperable systems <ul><li>S 1 -S n  – Enterprise Information Systems </li></ul><ul><li>...
Our approach to semantic interoperability in supply chain networks 1/2 <ul><li>Based on Supply Chain Operations Reference ...
Our approach to semantic interoperability in supply chain networks 2/2 SCOR- MAP SCOR-FULL OWL SCOR-SYS OWL SCOR-KOS OWL S...
Where is enterprise semantics ? <ul><li>Our assumptions in this approach: </li></ul><ul><ul><li>Enterprise realities are r...
Our approach to database-to-ontology mapping Database er.owl attribute constraint entity multiplicity relation type hasAtt...
Extraction of data from heterogeneous sources <ul><li>Use EISs (USE i ) to export data files (F i ) then transform and mer...
Semantic query <ul><li>A pair (O,C) </li></ul><ul><ul><li>O – set of concepts to be inferred </li></ul></ul><ul><ul><li>C ...
Semantic query execution <ul><li>Each SQL query returns data which is used to generate OWL statements which are asserted t...
Conclusions <ul><li>Widely adopted supply chain process reference model is used as a starting point for semantic interoper...
Gaps and future challenges <ul><li>Business logic is not explicated, its difficult to find it even in implicit form </li><...
Local Ontologies for Semantic Interoperability in Supply Chain Networks Milan Zdravković, Miroslav Trajanović University o...
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Milan Zdravkovic, Miroslav Trajanovic, Hervé Panetto, Local Ontologies for Semantic Interoperability in Supply Chain Networks

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13th International Conference on Enterprise Information Systems, ICEIS’2011, June 8-11, 2011, Beijing, P.R. China

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Milan Zdravkovic, Miroslav Trajanovic, Hervé Panetto, Local Ontologies for Semantic Interoperability in Supply Chain Networks

  1. 1. Local Ontologies for Semantic Interoperability in Supply Chain Networks Milan Zdravković, Miroslav Trajanović University of Niš, Serbia milan.zdravkovic@masfak.ni.ac.rs, traja@masfak.ni.ac.rs Hervé Panetto Research Centre for Automatic Control (CRAN – UMR 7039), Nancy-Université, CNRS, France [email_address] ICEIS’2011, June 8-11, 2011, Beijing, P.R. China
  2. 2. Problems of “traditional” supply chains <ul><li>High-speed, low-cost </li></ul><ul><li>Cost reduction is a key aspect of collaboration </li></ul><ul><ul><li>Reduced number of suppliers </li></ul></ul><ul><ul><li>Dyadic relationships management </li></ul></ul><ul><ul><li>High level of integration </li></ul></ul><ul><li>High costs </li></ul><ul><li>Reduced flexibility </li></ul>Often, SC can’t respond effectively to structural changes in demand
  3. 3. Virtual organizations – Supply chains of the future ? *Virtual Breeding Environment Ent 2 Ent 4 Ent 1 Ent 3 Ent 5 Ent 6 **Virtual Enterprise 1 Ent 21 Ent 41 Ent 11 Ent 31 Ent 61 **Virtual Enterprise n Ent 2n Ent 4n Ent 5n Ent 3n Opportunity 1 Opportunity n Selection Configuration Selection Configuration Dissolution Dissolution **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)
  4. 4. What is interoperability ? <ul><li>ISO/IEC 2382 </li></ul><ul><ul><li>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. </li></ul></ul><ul><li>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) </li></ul>
  5. 5. Is it easy ? English translation of Welsh: “I am not in the office at the moment. Please send any work to be translated”
  6. 6. What is Semantic Interoperability ? <ul><li>system(S) ∧ system(R) ∧ semantically-interoperable(S,R) ⇒ </li></ul><ul><li>∀ p ( </li></ul><ul><ul><li>(transmitted-from(p,S) ∧ transmitted-to(p,R)) ∧ ∀q(statement-of(q,S) ∧ p⇒q) ∃q’(statement-of(q’,R) ∧ p⇒q’ ∧ q’⇔q) </li></ul></ul><ul><ul><li>) </li></ul></ul>
  7. 7. Implementation of semantically interoperable systems <ul><li>S 1 -S n – Enterprise Information Systems </li></ul><ul><li>O L1 -O L2 – Local ontologies </li></ul><ul><li>O D1,2 – Domain ontologies </li></ul><ul><li>M LiDi – Mappings between local and domain ontologies </li></ul>O L1 O D1 O L2 M L1D1 M L2D1 M O1O2 ≡f(M L1D1 , M L2D1 ) S 1 S 2 C n C 1 C 2 M LnD1 S n O Ln M O1On ≡f(M L1D1 , M LnD1 ) O D2 S i O Li M LiD2 M D1D2 M O1Oi ≡f(M L1D1 , M D1D2 , M LiD2 )
  8. 8. Our approach to semantic interoperability in supply chain networks 1/2 <ul><li>Based on Supply Chain Operations Reference (SCOR) </li></ul><ul><ul><li>standard approach for analysis, design and implementation of five core processes in supply chains: plan, source, make, deliver and return </li></ul></ul><ul><ul><li>it defines a framework, which aims at integrating business processes, metrics, best practices and technologies with the objective to improve collaboration between partners </li></ul></ul><ul><li>SCOR model is implicit </li></ul><ul><ul><li>It is semantically enriched (SCOR-Full), when common general properties are recognized and used to aggregate the SCOR concepts into general notions </li></ul></ul><ul><li>Approach reduces the development time, as it builds upon the existing consensus of the domain experts, transposed into the SCOR reference model </li></ul>
  9. 9. Our approach to semantic interoperability in supply chain networks 2/2 SCOR- MAP SCOR-FULL OWL SCOR-SYS OWL SCOR-KOS OWL SCOR Native formats, Exchange formats Domain Ontologies 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 Semantic Query service EIS database LOCAL ONTOLOGY Transformation service EIS database LOCAL ONTOLOGY EIS database LOCAL ONTOLOGY
  10. 10. Where is enterprise semantics ? <ul><li>Our assumptions in this approach: </li></ul><ul><ul><li>Enterprise realities are represented by the corresponding enterprise information systems (EIS). </li></ul></ul><ul><ul><li>Enterprise message models are based on EISs’ data models, represented implicitly in their databases. </li></ul></ul><ul><li>Semantics of the business logic (from EISs) remains hidden </li></ul><ul><ul><li>with exception of database triggers, unless they are used to enforce referential integrity </li></ul></ul>
  11. 11. Our approach to database-to-ontology mapping Database er.owl attribute constraint entity multiplicity relation type hasAttribute hasType hasConstraint hasSourceAttribute hasDestinationAttribute hasSourceMultiplicity hasDestinationMultiplicity output imports s-er.owl concept hasObjectProperty data-type hasDataProperty data-concept hasDataType hasDefiningProperty hasDefiningDataProperty hasFunctionalProperty 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) Data import and classification of ER entities Classification (inference) of OWL types and properties Lexical Refinement Local ontology generation output
  12. 12. Extraction of data from heterogeneous sources <ul><li>Use EISs (USE i ) to export data files (F i ) then transform and merge </li></ul><ul><li>Execute SQL queries (SQL Qi ) to get result sets (R Si ) then merge </li></ul><ul><li>Execute DL queries (DL Qi ) to get sets of triples S Ti where resulting set is their union </li></ul><ul><li>Use dictionary to extract data with a single DL query </li></ul><ul><li>Use other dictionaries to extract the same data </li></ul>SCOR-MAP DOMAIN ONTOLOGY 1 Transform F 1 -F n to common format and merge to F USE 1 USE 2 USE n F 1 F 2 F n DL QD1 S T Merge R S1 -R Sn to R S EIS database EIS database EIS database SQL Q1 SQL Q2 SQL Qn R S1 R S2 R Sn S T ≡ S T1 U S T2 U S T3 LOCAL ONTOLOGY LOCAL ONTOLOGY LOCAL ONTOLOGY DL Q1 DL Q2 DL Qn S T1 S T2 S Tn DOMAIN ONTOLOGY 2 DOMAIN ONTOLOGY m DL QD2,.., DL QDm
  13. 13. Semantic query <ul><li>A pair (O,C) </li></ul><ul><ul><li>O – set of concepts to be inferred </li></ul></ul><ul><ul><li>C – set of restrictions to be applied on their properties </li></ul></ul><ul><li>Only restrictions can be used in queries, because concepts are inferred as property domains and ranges. </li></ul><ul><ul><li>e.g. hasAccountAccountType some (hasCode value 3) </li></ul></ul><ul><ul><li>However, in case of very general concepts, such as “name” or “id” this may be troublesome (or not ?) </li></ul></ul><ul><ul><ul><li>Domain of hasName property in openERP local ontology is union of 170 concepts </li></ul></ul></ul><ul><ul><ul><li>But, you don’t have to know anything about underlying ER model in order to launch queries </li></ul></ul></ul>
  14. 14. Semantic query execution <ul><li>Each SQL query returns data which is used to generate OWL statements which are asserted to a temporary model </li></ul><ul><li>Temporary model is a graph, which focal concept is bNode2 (first step), bNode1 (second step) and X (final step) </li></ul><ul><li>End result is a graph, which focal concept is X </li></ul>Input Query hasResCompany some (hasResCurrency some (hasName value &quot;EUR&quot;) ) Decomposition subject predicate some|only|min n|max m|exactly o bNode subject predicate value {type} X hasResCompany some bNode1 bNode1 hasResCurrency some bNode2 bNode2 hasName value &quot;EUR&quot; SQL construct and execute bNode2 nothing ? bNode1 nothing ? X nothing ? Assert to temporary mdl SQL construct and execute No Assert to temporary mdl SQL construct and execute No Yes Yes Assert to temporary mdl No Temp mdl is resulting mdl No result Yes
  15. 15. Conclusions <ul><li>Widely adopted supply chain process reference model is used as a starting point for semantic interoperability framework </li></ul><ul><ul><li>Weak consequences of “inspirational approach” avoided </li></ul></ul><ul><ul><li>Consensus on the model already exists in community </li></ul></ul><ul><ul><li>Development reduced – you don’t have to start from the scratch </li></ul></ul><ul><li>Enterprises are “introduced” to interoperable world in supply chain networks </li></ul><ul><ul><li>With their partial realities (EISs databases), explicitly represented (corresponded to a “common knowledge”) </li></ul></ul><ul><ul><li>Did we just described two Interoperability Service Utilities (ISU) – Transformation Service and “Ask” interface of Semantic Query Service ? </li></ul></ul>
  16. 16. Gaps and future challenges <ul><li>Business logic is not explicated, its difficult to find it even in implicit form </li></ul><ul><li>Semantics of the database data is not analyzed (e.g. based on data occurrence patterns) </li></ul><ul><li>Local ontology need to be enacted – in our approach it is considered only as an intermediary model </li></ul>
  17. 17. Local Ontologies for Semantic Interoperability in Supply Chain Networks Milan Zdravković, Miroslav Trajanović University of Niš, Serbia milan.zdravkovic@masfak.ni.ac.rs, traja@masfak.ni.ac.rs Hervé Panetto Research Centre for Automatic Control (CRAN – UMR 7039), Nancy-Université, CNRS, France [email_address] ICEIS’2011, June 8-11, 2011, Beijing, P.R. China Thank you for your attention

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