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Kumar Madurai

October 21, 2013

Knowledge EngineeringUsing Linked Data in an Enterprise

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Knowledge Engineering – Problem Context

• 15 different product definitions

• 17 different application systems creating design, regulatory, and production data

• 100’s of change requests monthly

• Tribal knowledge about product design and process design not captured anywhere

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Opportunities for Improvement

• How do we reduce the complexity?• How do we promote consistency?• How do we foster collaborative sharing of

data and knowledge?• How do we get an integrated view of

process and product data across the product life cycle?

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Knowledge Engineering Semantic Data Framework

Original Data

Sources

Database1 Database2

Staged Data in Oracle

OntologySemanticLayer in Oracle

Mapping Rules using D2RQ

Linked Concepts and Properties in Domain Views

Applications Consuming Domain Views

ValidatedSystems

Database3 Database4

Non-ValidatedSystems

Database1 Database2 Database3 Database4

Model1RDF Triples

Model2RDF Triples

Model3RDF Triples

Model4RDF Triples

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Relational Data Source - Simple Example

Equipment_Id Inspector_Id Inspection_Date

E02363 103546 01/10/2013

Employee_Id Employee Name Network_Id

103456 Joe Mathis jmathis

Document_Id Author_Id Creation_Date

D8946 jmathis 06/15/2012

EquipmentDatabase

DocumentDatabase

EmployeeDatabase

Query: Give me the document(s) authored by inspector of equipment E02363

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Ontology Model for Relational Example

m:Equipment m:equipmentIDrdfs:domain xsd:stringrdfs:range

m:inspectedByrdfs:domain m:Personrdfs:range

m:Document m:documentIDrdfs:domain xsd:stringrdfs:range

m:authoredByrdfs:domain m:Personrdfs:range

m:Employee m:employeeIDrdfs:domain xsd:stringrdfs:range

m:networkIDrdfs:domain xsd:stringrdfs:range

m:Person rdfs:subClassOf m:Employee

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Semantic Data for Relational Examplem:Equipment

:Equipment_E02363

rdf:type

:equipmentID

E02363

:inspectedBy

:Person_103546

:inspectionDate

01/10/2013

m:Document

:Document_D8946

rdf:type

D8946

:authoredBy

:Person_jmathis

:creationDate

06/15/2012

:documentID

m:Employee

:Person_103546

rdf:type

103546

:employeeName

Joe Mathis

:networkID

jmathis

:employeeID

Inference Rule Example

CONSTRUCT { ?emp2 rdf:type m:Employee . ?emp2 owl:sameAs ?emp1 }WHERE{ ?emp1 rdf:type m:Employee . ?emp1 m:networkID ?netID . BIND (URI (CONCAT (“http://KE/Data/SEM#”, “Person_”, ?netID)) AS ?emp2) }

m:Employee

:Person_103546

rdf:type

:networkID

jmathis

:Person_jmathis

owl:sameAs

rdf:type

:employeeName

Joe Mathis

With OWL Inferencing:SELECT ?nameWHERE{ :Document_D8946 :authoredBy ?author{ ?author :employeeName ?name }

Results in: nameJoe Mathis

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A Manufacturing Example

Prod_Id Prod_Name Mfg_LineP1 Splash Line #1P2 Trident Line #4

ManufacturingDatabase

PurchasingDatabase

Query: Give me the suppliers of raw materials for products made on Line #4

Product TableProd_Id Matl_Used Qty_Used

P1 Pink Colorant 20

P1 Silver Wrapper 1

P1 Melon Flavor 15

P2 Red Colorant 13

P2 Silver Wrapper 1

P2 Cherry Flavor 10

BOM Table

Matl_Name Supplier_Id

Colorant-Pink S1

Colorant-Red S1

Flavor-Melon S2

Flavor-Cherry S3

Wrapper-Silver S4

Raw Material TableSupplier_Id Supplier_Name

S1 Acme

S2 Foods-R-Us

S3 Yummy

S4 Lotus Inc.

Supplier Table

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Ontology Model for Manufacturing Database

m:Product m:productIDrdfs:domain rdfs:range

xsd:string

m:productNamerdfs:domain rdfs:range

m:hasManufacturingLine

rdfs:domain

rdfs:range m:ManufacturingLine

m:BillofMaterial rdfs:domain rdfs:range m:Product

rdfs:domainrdfs:range

rdfs:domain

rdfs:range

m:hasProduct

m:hasRawMaterial m:RawMaterial

m:qtyUsed xsd:floatrdfs:domain

m:unitOfMeasure rdfs:range xsd:string

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Ontology Model for Purchasing Database

p:RawMaterial p:materialNamerdfs:domain rdfs:range xsd:string

p:hasMaterialTyperdfs:domain rdfs:range

p:hasSupplier

rdfs:domain

rdfs:range p:Supplier

rdfs:domain

p:supplierID

rdfs:domain

p:supplierName

xsd:string

p:MaterialType

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m:Product_P1 rdf:type m:Product m:Product_P1 m:ProductID “P1”m:Product_P1 m:ProductName “Splash”m:Product_P1 m:hasManufacturingLine m:Line_1m:Product_P2 rdf:type m:Product m:Product_P2 m:ProductID “P2”m:Product_P2 m:ProductName “Trident”m:Product_P2 m:hasManufacturingLine m:Line_4

m:Line_1 rdf:type m:ManufacturingLinem:Line_1 rdfs:label “Line #1”m:Line_4 rdf:type m:ManufacturingLinem:Line_4 rdfs:label “Line #4”

m:Bom_P1_Pink_Colorant rdf:type m:BillOfMaterial m:Bom_P1_Pink_Colorant m:hasProduct m:Product_P1 m:Bom_P1_Pink_Colorant m:hasRawMaterial m:Material_Pink_Colorant m:Bom_P1_Pink_Colorant qtyUsed 20m:Bom_P1_Silver_Wrapper rdf:type m:BillOfMaterial m:Bom_P1_Silver_Wrapper m:hasProduct m:Product_P1 m:Bom_P1_Silver_Wrapper m:hasRawMaterial m:Material_Silver_Wrapper m:Bom_P1_Silver_Wrapper qtyUsed 1………………………..

Question: How do we update the unitOfMeasure property?

RDF Triples from D2RQ Mapping - Manufacturing Database

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RDF Triples from D2RQ Mapping - Purchasing Databasep:Material_Colorant_Pink rdf:type p:RawMaterial p:Material_Colorant_Pink p:materialName “Colorant-Pink”p:Material_Colorant_Pink p:hasMaterialType p:MaterialType_Colorant p:Material_Colorant_Pink p:hasSupplier p:Supplier_S1p:Material_Wrapper_Silver rdf:type p:RawMaterial p:Material_Wrapper_Silver p:materialName “Wrapper-Silver”p:Material_Wrapper_Silver p:hasMaterialType p:MaterialType_Wrapper p:Material_Wrapper_Silver p:hasSupplier p:Supplier_S4…………………………….

p:Supplier_S1 rdf:type p:Supplierp:Supplier_S1 p:supplierID “S1”p:Supplier_S1 p:supplierName “Acme”p:Supplier_S4 rdf:type p:Supplierp:Supplier_S4 p:supplierID “S4”p:Supplier_S4 p:supplierName “Lotus Inc.”……………………………

p:MaterialType_Colorant rdf:type p:MaterialType p:MaterialType_Colorant rdfs:label “Colorant”p:MaterialType_Wrapper rdf:type p:MaterialType p:MaterialType_Wrapper rdfs:label “Wrapper”…………………………….

Question: How do we link the materials from Purchasing to Manufacturing?13

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Data Traceability is Critical

• Ability to link any item used in an application to the exact data source all the way downstream – use of ‘hasDataSource’ property for every instance created

• Linking of data occurs in two levels, across the product genealogy (horizontal and business driven), and across the system layers (vertical and technology driven)

• Semantic relationships between concepts should be defined properly and maintained to reflect changes in underlying source systems

• Important to keep non-validated data in their own models (semantic graphs) especially in a regulated environment

• Specific verification / validation steps to be performed when new applications are brought on board using the semantic layer

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Conclusion / Takeaways• Ontological modeling of enterprise data stored in conventional

databases is the first and crucial step• Augmenting the model with rules adds more power to the

inferencing capabilities of the model• Annotation properties (rdfs:label, rdfs:comment, rdfs:seeAlso,

etc.) can also be used to add semantic meaning to the data• D2RQ provides a flexible mapping language and a set of tools to

enable the conversion of relational data to RDF triples • Judicious use of owl:sameAs helps in linking instances that are

the same but from different sources • Critical to ensure data traceability (also called data provenance)

which has to be planned for in the model and when data is loaded into the database