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Agile Enterprise OntologyAgile Enterprise OntologyAgile Enterprise OntologyAgile Enterprise Ontology
© 2015 Semantic Arts,...
ObjectivesObjectivesObjectivesObjectives
•Two Target AudiencesTwo Target AudiencesTwo Target AudiencesTwo Target Audiences...
Our JourneyOur JourneyOur JourneyOur Journey
•Over the last 15 years we’ve been designing and buildingOver the last 15 yea...
Enterprise OntologyEnterprise OntologyEnterprise OntologyEnterprise Ontology
•Analogous to an Enterprise Data ModelAnalogo...
How (Very Briefly)How (Very Briefly)How (Very Briefly)How (Very Briefly)
•URIsURIsURIsURIs
•TriplesTriplesTriplesTriples
•...
̬藐
The “URI” (Uniform Resource Identifier)The “URI” (Uniform Resource Identifier)The “URI” (Uniform Resource Identifier)Th...
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All Information Expressed as TriplesAll Information Expressed as TriplesAll Information Expressed as TriplesAll Informa...
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A Graph is a Self Assembling “Join”A Graph is a Self Assembling “Join”A Graph is a Self Assembling “Join”A Graph is a S...
Formal DefinitionFormal DefinitionFormal DefinitionFormal Definition
•The definition can be readThe definition can be read...
՜纰
Because typing is just another triple, any instance,Because typing is just another triple, any instance,Because typing ...
Common DenominatorCommon DenominatorCommon DenominatorCommon Denominator
:predicate
© 2015 Semantic Arts, Inc.
11
:subject...
Evolve in PlaceEvolve in PlaceEvolve in PlaceEvolve in Place
© 2015 Semantic Arts, Inc. 12
Փ毐
In the Process of Building EnterpriseIn the Process of Building EnterpriseIn the Process of Building EnterpriseIn the P...
We DiscoveredWe DiscoveredWe DiscoveredWe Discovered
•Hard to SellHard to SellHard to SellHard to Sell
•Hard for others to...
•We (Semantic Arts) have arrived at this conclusion:We (Semantic Arts) have arrived at this conclusion:We (Semantic Arts) ...
Along the wayAlong the wayAlong the wayAlong the way
© 2015 Semantic Arts, Inc. 16
http://semanticarts.com/gist
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Early ProjectsEarly ProjectsEarly ProjectsEarly Projects
•About half the EOAbout half the EOAbout half the EOAbout half ...
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EvolutionEvolutionEvolutionEvolution
•We evolved gist and our methodologyWe evolved gist and our methodologyWe evolved ...
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Current ProjectsCurrent ProjectsCurrent ProjectsCurrent Projects
•Most classesMost classesMost classesMost classes
deri...
MeanwhileMeanwhileMeanwhileMeanwhile
•It was obvious that our clients wanted:It was obvious that our clients wanted:It was...
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The Dark Side of AgileThe Dark Side of AgileThe Dark Side of AgileThe Dark Side of Agile
© 2015 Semantic Arts, Inc. 21
զ睰
What they really wantWhat they really wantWhat they really wantWhat they really want
•Incremental way to get to integra...
՟甀
We Now ProposeWe Now ProposeWe Now ProposeWe Now Propose
Enterprise Ontology
© 2015 Semantic Arts, Inc. 23
7 weeks
Ente...
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Followed byFollowed byFollowed byFollowed by
Enterprise Ontology
SubDomainOntology
© 2015 Semantic Arts, Inc.
24
6-12 w...
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Leading ToLeading ToLeading ToLeading To Enterprise Ontology
SubDomainOntology
Enterprise Ontology
Enterprise Ontology
S...
Ֆ
What We’ve Learned Along the WayWhat We’ve Learned Along the WayWhat We’ve Learned Along the WayWhat We’ve Learned Along...
Methodology: Comparing Four Sources ofMethodology: Comparing Four Sources ofMethodology: Comparing Four Sources ofMethodol...
Ֆ
HarvestHarvestHarvestHarvest
•Since we’re eventually going to be using our ontology toSince we’re eventually going to be...
Standards BasedStandards BasedStandards BasedStandards Based
•Industry standards for data communication are importantIndus...
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Facilitated DiscoveryFacilitated DiscoveryFacilitated DiscoveryFacilitated Discovery
Interview
Author OWL
Run inference ...
Postulate and TestPostulate and TestPostulate and TestPostulate and Test
– gist Finance
Author : mkumbaDave McComb
Last Up...
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DifferencesDifferencesDifferencesDifferences
Harvest Standards Facilitated Postulate
Time Frame With Machine
Learning c...
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Reducing Cognitive LoadReducing Cognitive LoadReducing Cognitive LoadReducing Cognitive Load
•# of things you must know...
︀︀
Economizing PropertiesEconomizing PropertiesEconomizing PropertiesEconomizing Properties
•Typical large enterprise has ...
︀︀
Subclass Assertions v. InferenceSubclass Assertions v. InferenceSubclass Assertions v. InferenceSubclass Assertions v. ...
︀︀
Key Concept: The DistinctionaryKey Concept: The DistinctionaryKey Concept: The DistinctionaryKey Concept: The Distincti...
︀︀
Definitions v. DescriptionsDefinitions v. DescriptionsDefinitions v. DescriptionsDefinitions v. Descriptions
•A “descri...
︀︀
Descriptions v. DefinitionsDescriptions v. DefinitionsDescriptions v. DefinitionsDescriptions v. Definitions
(N) hc:rec...
︀︀
The “Sketch”The “Sketch”The “Sketch”The “Sketch”
•We’ve found the discipline of creating subsets of theWe’ve found the ...
︀︀
Ontology Sketch (aka the “Placemat”)Ontology Sketch (aka the “Placemat”)Ontology Sketch (aka the “Placemat”)Ontology Sk...
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Agile Detecting ErrorsAgile Detecting ErrorsAgile Detecting ErrorsAgile Detecting Errors
•The “easy to change” aspect o...
DisjointsDisjointsDisjointsDisjoints
•Most of the errors that a tableaux reasoned will surfaceMost of the errors that a ta...
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DisjointnessDisjointnessDisjointnessDisjointness as Venn Diagramsas Venn Diagramsas Venn Diagramsas Venn Diagrams
© 201...
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High Level DisjointsHigh Level DisjointsHigh Level DisjointsHigh Level Disjoints
Person GeoRegionX
© 2015 Semantic Arts...
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ABoxABoxABoxABox Unit TestsUnit TestsUnit TestsUnit Tests
•Check for things that should not arise in the course ofCheck...
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Fractal ModelingFractal ModelingFractal ModelingFractal Modeling
•Move most of your taxonomies out of the class structu...
՞쵰
Move Minor Distinctions to TaxonomiesMove Minor Distinctions to TaxonomiesMove Minor Distinctions to TaxonomiesMove Min...
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Should it be in the taxonomy?Should it be in the taxonomy?Should it be in the taxonomy?Should it be in the taxonomy?
•S...
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Classes and Taxonomic InstancesClasses and Taxonomic InstancesClasses and Taxonomic InstancesClasses and Taxonomic Inst...
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ModularityModularityModularityModularity –––– a Two Edged Sworda Two Edged Sworda Two Edged Sworda Two Edged Sword
Allo...
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Rules for working with modulesRules for working with modulesRules for working with modulesRules for working with module...
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Namespaces and ModulesNamespaces and ModulesNamespaces and ModulesNamespaces and Modules
•Many ontologies use many name...
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Namespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our Recommendatio...
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Namespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our Recommendatio...
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Separation of ConcernsSeparation of ConcernsSeparation of ConcernsSeparation of Concerns
•Meaning (OWL) Structure (RDF ...
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RDF Shape ExampleRDF Shape ExampleRDF Shape ExampleRDF Shape Example
<ProductRef> {
rdf:type (spo:ProductReference)
, g...
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SummarySummarySummarySummary
•It is necessary (and luckily possible) to create enterpriseIt is necessary (and luckily p...
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Question TimeQuestion TimeQuestion TimeQuestion Time
•To pursue this further:To pursue this further:To pursue this furt...
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Smart Data Webinar (SLIDES): Agile Enterprise Ontology

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An Enterprise Ontology is a formal representation of the meaning of an enterprise's information. Historically creating one has been a laborious process that took many months, often years to complete.

Recent advances in technology and methodology have now made it possible to build a core enterprise ontology in a matter of weeks, and to refine it gracefully over time. In this presentation we will describe what an enterprise ontology is and distinguish it from a conceptual or logical data model. We will present how the combination of structure free and formal inclusion gives power and flexibility simultaneously.

We will show through case studies how companies have been able to build ontologies rapidly and how they have leverage their enterprise ontology onto such diverse initiatives as: Enterprise Message Modeling, Integrating Structured and Unstructured Data, Model Driven Application Development and Master Data Management.

After attending this webinar, you will be able to:

* Describe what an Enterprise Ontology is
* Explain the value in creating an Enterprise Ontology
* Outline the approach and prerequisites for creating an Enterprise Ontology
* Suggest projects that could be greatly enhanced by applying an Enterprise Ontology

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Smart Data Webinar (SLIDES): Agile Enterprise Ontology

  1. 1. Agile Enterprise OntologyAgile Enterprise OntologyAgile Enterprise OntologyAgile Enterprise Ontology © 2015 Semantic Arts, Inc. Dave McCombDave McCombDave McCombDave McComb Semantic ArtsSemantic ArtsSemantic ArtsSemantic Arts May 14, 2015May 14, 2015May 14, 2015May 14, 2015 DataversityDataversityDataversityDataversity
  2. 2. ObjectivesObjectivesObjectivesObjectives •Two Target AudiencesTwo Target AudiencesTwo Target AudiencesTwo Target Audiences oThose who want to know that it’s possible to build an enterpriseThose who want to know that it’s possible to build an enterpriseThose who want to know that it’s possible to build an enterpriseThose who want to know that it’s possible to build an enterprise ontology in a limited amount of timeontology in a limited amount of timeontology in a limited amount of timeontology in a limited amount of time oThose who want to know howThose who want to know howThose who want to know howThose who want to know how •Goal: Provide insights to improve ontology development toGoal: Provide insights to improve ontology development to © 2015 Semantic Arts, Inc. •Goal: Provide insights to improve ontology development toGoal: Provide insights to improve ontology development toGoal: Provide insights to improve ontology development toGoal: Provide insights to improve ontology development to make your ontology:make your ontology:make your ontology:make your ontology: oEasier to extendEasier to extendEasier to extendEasier to extend oStable in the face of changeStable in the face of changeStable in the face of changeStable in the face of change oSimpler for others to understandSimpler for others to understandSimpler for others to understandSimpler for others to understand 2
  3. 3. Our JourneyOur JourneyOur JourneyOur Journey •Over the last 15 years we’ve been designing and buildingOver the last 15 years we’ve been designing and buildingOver the last 15 years we’ve been designing and buildingOver the last 15 years we’ve been designing and building ontologiesontologiesontologiesontologies for a number of large firms in many differentfor a number of large firms in many differentfor a number of large firms in many differentfor a number of large firms in many different industriesindustriesindustriesindustries © 2015 Semantic Arts, Inc. 3
  4. 4. Enterprise OntologyEnterprise OntologyEnterprise OntologyEnterprise Ontology •Analogous to an Enterprise Data ModelAnalogous to an Enterprise Data ModelAnalogous to an Enterprise Data ModelAnalogous to an Enterprise Data Model •Important distinctions stem from being rooted in SemanticImportant distinctions stem from being rooted in SemanticImportant distinctions stem from being rooted in SemanticImportant distinctions stem from being rooted in Semantic Technology:Technology:Technology:Technology: oA comprehensive model can be much, much simplerA comprehensive model can be much, much simplerA comprehensive model can be much, much simplerA comprehensive model can be much, much simpler © 2015 Semantic Arts, Inc. oA comprehensive model can be much, much simplerA comprehensive model can be much, much simplerA comprehensive model can be much, much simplerA comprehensive model can be much, much simpler oYet less abstract, and less ambiguousYet less abstract, and less ambiguousYet less abstract, and less ambiguousYet less abstract, and less ambiguous oIt can be directly implementedIt can be directly implementedIt can be directly implementedIt can be directly implemented oIt can evolve in placeIt can evolve in placeIt can evolve in placeIt can evolve in place oIt can integrate structured, unstructured and semiIt can integrate structured, unstructured and semiIt can integrate structured, unstructured and semiIt can integrate structured, unstructured and semi----structuredstructuredstructuredstructured datadatadatadata 4
  5. 5. How (Very Briefly)How (Very Briefly)How (Very Briefly)How (Very Briefly) •URIsURIsURIsURIs •TriplesTriplesTriplesTriples •GraphsGraphsGraphsGraphs •Formal DefinitionsFormal DefinitionsFormal DefinitionsFormal Definitions © 2015 Semantic Arts, Inc. •Formal DefinitionsFormal DefinitionsFormal DefinitionsFormal Definitions •MultiMultiMultiMulti----typingtypingtypingtyping 5
  6. 6. ̬藐 The “URI” (Uniform Resource Identifier)The “URI” (Uniform Resource Identifier)The “URI” (Uniform Resource Identifier)The “URI” (Uniform Resource Identifier) •This identifier is truly globalThis identifier is truly globalThis identifier is truly globalThis identifier is truly global •It means the same thing, regardless of the database ofIt means the same thing, regardless of the database ofIt means the same thing, regardless of the database ofIt means the same thing, regardless of the database of document it is part ofdocument it is part ofdocument it is part ofdocument it is part of © 2015 Semantic Arts, Inc. 6 cusip:02209S103 http://cusip.com/reg#02209S103=
  7. 7. ՞믰 All Information Expressed as TriplesAll Information Expressed as TriplesAll Information Expressed as TriplesAll Information Expressed as Triples :owns © 2015 Semantic Arts, Inc. 7 :dave :PU27 :owns :trade1 :occurredOn “12-25-2013”
  8. 8. ՛Ā A Graph is a Self Assembling “Join”A Graph is a Self Assembling “Join”A Graph is a Self Assembling “Join”A Graph is a Self Assembling “Join” •Which may have been harvested from completelyWhich may have been harvested from completelyWhich may have been harvested from completelyWhich may have been harvested from completely different sourcesdifferent sourcesdifferent sourcesdifferent sources cusip:02209S103 cusip:filedBy cusip:02209S © 2015 Semantic Arts, Inc. 8 cusip:name “Altria” cusip:02209S cusip:02209S foaf:name “Phillip Morris”cusip:02209S103 cusip:123456 cusip:underwrittenBy
  9. 9. Formal DefinitionFormal DefinitionFormal DefinitionFormal Definition •The definition can be readThe definition can be readThe definition can be readThe definition can be read and processed by aand processed by aand processed by aand processed by a system (as well as asystem (as well as asystem (as well as asystem (as well as a human)human)human)human) gist:Person hc:received some hc:MedicalTreatment hc:Patient --- AND --- © 2015 Semantic Arts, Inc. •Consistent definitionConsistent definitionConsistent definitionConsistent definition •Plus inference of newPlus inference of newPlus inference of newPlus inference of new information from existinginformation from existinginformation from existinginformation from existing informationinformationinformationinformation 9
  10. 10. ՜纰 Because typing is just another triple, any instance,Because typing is just another triple, any instance,Because typing is just another triple, any instance,Because typing is just another triple, any instance, can, and usually is, a member of many classescan, and usually is, a member of many classescan, and usually is, a member of many classescan, and usually is, a member of many classes simultaneouslysimultaneouslysimultaneouslysimultaneously :Person :Parent :GrandParent © 2015 Semantic Arts, Inc. :John :Dave :parentOf :Addie :parentOf :isa :isa :isa
  11. 11. Common DenominatorCommon DenominatorCommon DenominatorCommon Denominator :predicate © 2015 Semantic Arts, Inc. 11 :subject :object
  12. 12. Evolve in PlaceEvolve in PlaceEvolve in PlaceEvolve in Place © 2015 Semantic Arts, Inc. 12
  13. 13. Փ毐 In the Process of Building EnterpriseIn the Process of Building EnterpriseIn the Process of Building EnterpriseIn the Process of Building Enterprise OntologiesOntologiesOntologiesOntologies © 2015 Semantic Arts, Inc. •4444----6 Months6 Months6 Months6 Months •~800 concepts~800 concepts~800 concepts~800 concepts 13 Classes 574 Object Properties 250 Data Type Properties 38 Total T-Box Axioms 1470
  14. 14. We DiscoveredWe DiscoveredWe DiscoveredWe Discovered •Hard to SellHard to SellHard to SellHard to Sell •Hard for others to understandHard for others to understandHard for others to understandHard for others to understand •Limited ImplementationLimited ImplementationLimited ImplementationLimited Implementation © 2015 Semantic Arts, Inc. 14
  15. 15. •We (Semantic Arts) have arrived at this conclusion:We (Semantic Arts) have arrived at this conclusion:We (Semantic Arts) have arrived at this conclusion:We (Semantic Arts) have arrived at this conclusion: © 2015 Semantic Arts, Inc. 15 In order to be adopted,In order to be adopted,In order to be adopted,In order to be adopted, ontologiesontologiesontologiesontologies need to be more agile.need to be more agile.need to be more agile.need to be more agile.
  16. 16. Along the wayAlong the wayAlong the wayAlong the way © 2015 Semantic Arts, Inc. 16 http://semanticarts.com/gist
  17. 17. ՜ Early ProjectsEarly ProjectsEarly ProjectsEarly Projects •About half the EOAbout half the EOAbout half the EOAbout half the EO classes wereclasses wereclasses wereclasses were derived from gistderived from gistderived from gistderived from gist © 2015 Semantic Arts, Inc. 17
  18. 18. զ睰 EvolutionEvolutionEvolutionEvolution •We evolved gist and our methodologyWe evolved gist and our methodologyWe evolved gist and our methodologyWe evolved gist and our methodology © 2015 Semantic Arts, Inc. 18
  19. 19. 匀# Current ProjectsCurrent ProjectsCurrent ProjectsCurrent Projects •Most classesMost classesMost classesMost classes derived from gist,derived from gist,derived from gist,derived from gist, without even tryingwithout even tryingwithout even tryingwithout even trying © 2015 Semantic Arts, Inc. 19
  20. 20. MeanwhileMeanwhileMeanwhileMeanwhile •It was obvious that our clients wanted:It was obvious that our clients wanted:It was obvious that our clients wanted:It was obvious that our clients wanted: oLess up front investmentLess up front investmentLess up front investmentLess up front investment oSomething their staff could understandSomething their staff could understandSomething their staff could understandSomething their staff could understand oSomething that put them on a path toward integrationSomething that put them on a path toward integrationSomething that put them on a path toward integrationSomething that put them on a path toward integration © 2015 Semantic Arts, Inc. oSomething that put them on a path toward integrationSomething that put them on a path toward integrationSomething that put them on a path toward integrationSomething that put them on a path toward integration oSomething that was stable even while changingSomething that was stable even while changingSomething that was stable even while changingSomething that was stable even while changing •In other words: AgileIn other words: AgileIn other words: AgileIn other words: Agile 20
  21. 21. ՜ The Dark Side of AgileThe Dark Side of AgileThe Dark Side of AgileThe Dark Side of Agile © 2015 Semantic Arts, Inc. 21
  22. 22. զ睰 What they really wantWhat they really wantWhat they really wantWhat they really want •Incremental way to get to integratedIncremental way to get to integratedIncremental way to get to integratedIncremental way to get to integrated © 2015 Semantic Arts, Inc. 22
  23. 23. ՟甀 We Now ProposeWe Now ProposeWe Now ProposeWe Now Propose Enterprise Ontology © 2015 Semantic Arts, Inc. 23 7 weeks Enterprise Ontology
  24. 24. 匀# Followed byFollowed byFollowed byFollowed by Enterprise Ontology SubDomainOntology © 2015 Semantic Arts, Inc. 24 6-12 weeks SubDomainOntology
  25. 25. ՜ Leading ToLeading ToLeading ToLeading To Enterprise Ontology SubDomainOntology Enterprise Ontology Enterprise Ontology SubDomainOntology Architecture Legacy Modernization / Replacement Projects Integration © 2015 Semantic Arts, Inc. 25 SubDomainOntology Enterprise Ontology SubDomainOntology SubDomainOntology Architecture New Sources: Big Data, Social, Unstructured
  26. 26. Ֆ What We’ve Learned Along the WayWhat We’ve Learned Along the WayWhat We’ve Learned Along the WayWhat We’ve Learned Along the Way © 2015 Semantic Arts, Inc. 26
  27. 27. Methodology: Comparing Four Sources ofMethodology: Comparing Four Sources ofMethodology: Comparing Four Sources ofMethodology: Comparing Four Sources of KnowledgeKnowledgeKnowledgeKnowledge •Harvesting from existing data basesHarvesting from existing data basesHarvesting from existing data basesHarvesting from existing data bases •Derive from standardsDerive from standardsDerive from standardsDerive from standards •Facilitate DiscoveryFacilitate DiscoveryFacilitate DiscoveryFacilitate Discovery •Postulate and TestPostulate and TestPostulate and TestPostulate and Test © 2015 Semantic Arts, Inc. •Postulate and TestPostulate and TestPostulate and TestPostulate and Test 27
  28. 28. Ֆ HarvestHarvestHarvestHarvest •Since we’re eventually going to be using our ontology toSince we’re eventually going to be using our ontology toSince we’re eventually going to be using our ontology toSince we’re eventually going to be using our ontology to integrating all these disparate systems…integrating all these disparate systems…integrating all these disparate systems…integrating all these disparate systems… © 2015 Semantic Arts, Inc. 28 •Why not just start there?Why not just start there?Why not just start there?Why not just start there?
  29. 29. Standards BasedStandards BasedStandards BasedStandards Based •Industry standards for data communication are importantIndustry standards for data communication are importantIndustry standards for data communication are importantIndustry standards for data communication are important •And it’s tempting to want to start with themAnd it’s tempting to want to start with themAnd it’s tempting to want to start with themAnd it’s tempting to want to start with them •But in our experience its not been a good place to startBut in our experience its not been a good place to startBut in our experience its not been a good place to startBut in our experience its not been a good place to start •They typically cover a small percent of what you will needThey typically cover a small percent of what you will needThey typically cover a small percent of what you will needThey typically cover a small percent of what you will need © 2015 Semantic Arts, Inc. •They typically cover a small percent of what you will needThey typically cover a small percent of what you will needThey typically cover a small percent of what you will needThey typically cover a small percent of what you will need to cover for your internal operationsto cover for your internal operationsto cover for your internal operationsto cover for your internal operations •They aren’t very rigorousThey aren’t very rigorousThey aren’t very rigorousThey aren’t very rigorous •And often unnecessarily complexAnd often unnecessarily complexAnd often unnecessarily complexAnd often unnecessarily complex 29
  30. 30. Ֆ Facilitated DiscoveryFacilitated DiscoveryFacilitated DiscoveryFacilitated Discovery Interview Author OWL Run inference to check consistency View in ontology editor © 2015 Semantic Arts, Inc. Interview Inferencing/Consistency check consistency Debug cycle Interview/Model cycle
  31. 31. Postulate and TestPostulate and TestPostulate and TestPostulate and Test – gist Finance Author : mkumbaDave McComb Last Updated : 3/21/2015 gist http://ontologies.semanticarts.com/gist# Namespaces URI : http://ontologies.semanticarts.com/o/gistTop7.1.1 Location : gistTop7.1.1.owl Imports gist:owns some gist:FinancialAccount gist:Person gist:Organization --- OR --- Equivalent to --- AND --- gist:Party gist:Account gist:hasDirectPart some gist:Position gist:ownedBy some gist:Party Equivalent to --- AND --- gist:FinancialAccount gist:Account gist:positionIn some gist:FinancialInstrument Equivalent to --- AND --- gist:Position gist:positionInThe relationship that describes what the position is "in" (need something better here) gist:Template gist:hasDirectPart some gist:Right gist:hasDirectPart some gist:Obligation Equivalent to --- AND --- gist:InvestmentInstrument gist:Position gist:reportingUoM some gist:CurrencyUnit gist:positionIn some gist:CurrencyUnit Equivalent to --- AND --- gist:CashPosition gist:Position gist:reportingUoM some gist:CurrencyUnit gist:positionIn some gist:InvestmentInstrument Equivalent to --- AND --- gist:InvestedPosition gist:InvestmentInstrument gist:CurrencyUnit Equivalent to --- OR --- gist:FinancialInstrument gist:InvestmentInstrument gist:tradedOn some gist:Exchange Equivalent to --- AND --- gist:ExchangeTradedInstrument gist:tradedOnan Exchange that makes a market for a particular instrument gist:reportingUoMThe currency the positions are reporting in (note: you can have a euro denominate note reported in US$) Research why I’m importing gistMag and gistMag7.1.1 © 2015 Semantic Arts, Inc. 31 dbbo:dave dbbo:PU27 dbbo:owns dbbo:PU29 dbbo:owns “Dave McComb” dbbo:in dbbo:Fleet2
  32. 32. զ睰 DifferencesDifferencesDifferencesDifferences Harvest Standards Facilitated Postulate Time Frame With Machine Learning can be done in months Time to understand standards often months Months Weeks Number of concepts to deal with Millions - > tens of thousands Thousands Up to a thousand A few hundred © 2015 Semantic Arts, Inc. 32 Scope Existing systems, not planned or data not in systems What Standards cover, often not whats needed to run business Depends on the SMEs invited Depends on scope postulated Preferred Sequence 1. First2. For detail, or specific applications 3. When ready to integrate with standards 4. To check coverage and prepare for large scale integration
  33. 33. 匀# Reducing Cognitive LoadReducing Cognitive LoadReducing Cognitive LoadReducing Cognitive Load •# of things you must know to be competent with the# of things you must know to be competent with the# of things you must know to be competent with the# of things you must know to be competent with the ontologyontologyontologyontology •# of things you must be in agreement with in order to# of things you must be in agreement with in order to# of things you must be in agreement with in order to# of things you must be in agreement with in order to commit to the ontologycommit to the ontologycommit to the ontologycommit to the ontology © 2015 Semantic Arts, Inc. commit to the ontologycommit to the ontologycommit to the ontologycommit to the ontology •# of concepts shared# of concepts shared# of concepts shared# of concepts shared 33
  34. 34. ︀︀ Economizing PropertiesEconomizing PropertiesEconomizing PropertiesEconomizing Properties •Typical large enterprise has millions of propertiesTypical large enterprise has millions of propertiesTypical large enterprise has millions of propertiesTypical large enterprise has millions of properties (attributes/ columns ) in their legacy systems(attributes/ columns ) in their legacy systems(attributes/ columns ) in their legacy systems(attributes/ columns ) in their legacy systems •This is mostly a product of arbitrary design decisionsThis is mostly a product of arbitrary design decisionsThis is mostly a product of arbitrary design decisionsThis is mostly a product of arbitrary design decisions •We need to be vigilantWe need to be vigilantWe need to be vigilantWe need to be vigilant © 2015 Semantic Arts, Inc. •We need to be vigilantWe need to be vigilantWe need to be vigilantWe need to be vigilant •Properties (with a few very narrow exceptions) can not beProperties (with a few very narrow exceptions) can not beProperties (with a few very narrow exceptions) can not beProperties (with a few very narrow exceptions) can not be formally defined, we must learn them allformally defined, we must learn them allformally defined, we must learn them allformally defined, we must learn them all •Our target is to get to a few hundredOur target is to get to a few hundredOur target is to get to a few hundredOur target is to get to a few hundred 34
  35. 35. ︀︀ Subclass Assertions v. InferenceSubclass Assertions v. InferenceSubclass Assertions v. InferenceSubclass Assertions v. Inference •We advocate using subclass assertions sparinglyWe advocate using subclass assertions sparinglyWe advocate using subclass assertions sparinglyWe advocate using subclass assertions sparingly •It typically is a carry over from Object OrientedIt typically is a carry over from Object OrientedIt typically is a carry over from Object OrientedIt typically is a carry over from Object Oriented •Or taxonomy designOr taxonomy designOr taxonomy designOr taxonomy design •Or reluctance to take a standOr reluctance to take a standOr reluctance to take a standOr reluctance to take a stand © 2015 Semantic Arts, Inc. •Or reluctance to take a standOr reluctance to take a standOr reluctance to take a standOr reluctance to take a stand 35 Party PersonOrg Party Person Org OR
  36. 36. ︀︀ Key Concept: The DistinctionaryKey Concept: The DistinctionaryKey Concept: The DistinctionaryKey Concept: The Distinctionary Is: a glossary Is distinct from other glossaries: structurally, each definition first specifies the more general type of © 2015 Semantic Arts, Inc. specifies the more general type of thing the word is, and then provides a way to distinguish this thing from others that are similar.
  37. 37. ︀︀ Definitions v. DescriptionsDefinitions v. DescriptionsDefinitions v. DescriptionsDefinitions v. Descriptions •A “description” tells us properties about a class ofA “description” tells us properties about a class ofA “description” tells us properties about a class ofA “description” tells us properties about a class of instances, if we already know what type the instance isinstances, if we already know what type the instance isinstances, if we already know what type the instance isinstances, if we already know what type the instance is •Adding classes to your ontology that are only descriptiveAdding classes to your ontology that are only descriptiveAdding classes to your ontology that are only descriptiveAdding classes to your ontology that are only descriptive increases cognitive load. You audience must know theincreases cognitive load. You audience must know theincreases cognitive load. You audience must know theincreases cognitive load. You audience must know the © 2015 Semantic Arts, Inc. increases cognitive load. You audience must know theincreases cognitive load. You audience must know theincreases cognitive load. You audience must know theincreases cognitive load. You audience must know the class and its (implicit) membership criteriaclass and its (implicit) membership criteriaclass and its (implicit) membership criteriaclass and its (implicit) membership criteria •A “definition” supplies the criteria for membership in aA “definition” supplies the criteria for membership in aA “definition” supplies the criteria for membership in aA “definition” supplies the criteria for membership in a class.class.class.class. •If your audience understands the criteria, they agree withIf your audience understands the criteria, they agree withIf your audience understands the criteria, they agree withIf your audience understands the criteria, they agree with the definition (they may not like the label)the definition (they may not like the label)the definition (they may not like the label)the definition (they may not like the label) 37
  38. 38. ︀︀ Descriptions v. DefinitionsDescriptions v. DefinitionsDescriptions v. DefinitionsDescriptions v. Definitions (N) hc:received some hc:MedicalTreatment hc:Patient gist:Person gist:Person hc:received some hc:MedicalTreatment hc:Patient --- AND --- Description Definition © 2015 Semantic Arts, Inc. 38 If you know someone is a Patient, then you know they are a Person And they must have had some treatment, even if you don’t know what it was But, how do you know someone is a patient? System can’t help you, you have to know If you knew someone was a patient, you’d know all the same information from the Description example Additionally, if you know someone is a Person and has received treatment the system will infer they are a Patient And the system will automatically, and correctly, keep track of the subclass hierarchy
  39. 39. ︀︀ The “Sketch”The “Sketch”The “Sketch”The “Sketch” •We’ve found the discipline of creating subsets of theWe’ve found the discipline of creating subsets of theWe’ve found the discipline of creating subsets of theWe’ve found the discipline of creating subsets of the ontology for the purpose of explainingontology for the purpose of explainingontology for the purpose of explainingontology for the purpose of explaining •Improves understandingImproves understandingImproves understandingImproves understanding •Also improves the building processAlso improves the building processAlso improves the building processAlso improves the building process © 2015 Semantic Arts, Inc. •Also improves the building processAlso improves the building processAlso improves the building processAlso improves the building process 39
  40. 40. ︀︀ Ontology Sketch (aka the “Placemat”)Ontology Sketch (aka the “Placemat”)Ontology Sketch (aka the “Placemat”)Ontology Sketch (aka the “Placemat”) SentaraOffer Care Provision Health Plan Benefit regarding Employee Benefit Employee Employment Contract Capability governs Payroll Disbursement Membership Premium Marketing Message Marketing Communication Sentara Target Market Business Referral refersTo Employment Underwriting Claim Insurance Product Regulation Regulator produce Sentara Healthcare Enterprise Ontology: Concise Overwiew May 2013 Regulations Marketing Legend Class SubClass Class Important SubClassClosely Related Class Class SubClass Abstract Superclass Another Classproperty not explicit Health Plan Policy Agreement Health Insurance Company Patient Person Goal BeHealthy Image Goal Provider Network © 2015 Semantic Arts, Inc. 40 Patient Visit Hospital Contractor Outcome Hospital Accreditation Hospital License Credentialing Organization giver owns Physical Location hasPhysical Location Sentara OrganizationProcedure Specification regarding Building occupies hiresForWork employshasA Recommending Treatment Prescription categorizedBy Patient Bill Health Product Or Service Delivery Preference Insurance Care Resources & Supplies regarding Supplier Care Instructions Body of Knowledge Diagnosing Medical Procedure Medical Treatment Patient Registered Patient Diagnosis Medical Condition Disease Physician Healthcare Provider Nurse Care Coordinator Human Resource Resource Healthcare Facility Equipment and Tools Supplier Agreement Sentara Vendor Agreement Pricing Obligation Volume Obligation In Hospital Care Delivery Mode Physician Office
  41. 41. զ睰 Agile Detecting ErrorsAgile Detecting ErrorsAgile Detecting ErrorsAgile Detecting Errors •The “easy to change” aspect of agile, requires a way toThe “easy to change” aspect of agile, requires a way toThe “easy to change” aspect of agile, requires a way toThe “easy to change” aspect of agile, requires a way to detect errors.detect errors.detect errors.detect errors. •Two of the more effective that we use areTwo of the more effective that we use areTwo of the more effective that we use areTwo of the more effective that we use are oHigh Level DisjointsHigh Level DisjointsHigh Level DisjointsHigh Level Disjoints © 2015 Semantic Arts, Inc. oHigh Level DisjointsHigh Level DisjointsHigh Level DisjointsHigh Level Disjoints oABoxABoxABoxABox Unit TestsUnit TestsUnit TestsUnit Tests 41
  42. 42. DisjointsDisjointsDisjointsDisjoints •Most of the errors that a tableaux reasoned will surfaceMost of the errors that a tableaux reasoned will surfaceMost of the errors that a tableaux reasoned will surfaceMost of the errors that a tableaux reasoned will surface stem fromstem fromstem fromstem from disjointnessdisjointnessdisjointnessdisjointness (or negation/ complement)(or negation/ complement)(or negation/ complement)(or negation/ complement) asssertionsasssertionsasssertionsasssertions •NoNoNoNo disjointnessdisjointnessdisjointnessdisjointness = no error checking= no error checking= no error checking= no error checking © 2015 Semantic Arts, Inc. •NoNoNoNo disjointnessdisjointnessdisjointnessdisjointness = no error checking= no error checking= no error checking= no error checking 42
  43. 43. ՞쵰 DisjointnessDisjointnessDisjointnessDisjointness as Venn Diagramsas Venn Diagramsas Venn Diagramsas Venn Diagrams © 2015 Semantic Arts, Inc. 43
  44. 44. ՞쵰 High Level DisjointsHigh Level DisjointsHigh Level DisjointsHigh Level Disjoints Person GeoRegionX © 2015 Semantic Arts, Inc. 44 Comedian Chevy Chase Chevy Chase City X
  45. 45. Ք︀ ABoxABoxABoxABox Unit TestsUnit TestsUnit TestsUnit Tests •Check for things that should not arise in the course ofCheck for things that should not arise in the course ofCheck for things that should not arise in the course ofCheck for things that should not arise in the course of using the ontology, use for unit testingusing the ontology, use for unit testingusing the ontology, use for unit testingusing the ontology, use for unit testing © 2015 Semantic Arts, Inc. 45 ASK { ?tc rdf:type sa:TimeCharge . ?tc gist:actualStart ?t1 . ?t1 gist:universalDateTime ?start . ?tc gist:actualEnd ?t2 . ?t2 gist:universalDateTime ?end . FILTER(?end < ?start) }
  46. 46. Ք︀ Fractal ModelingFractal ModelingFractal ModelingFractal Modeling •Move most of your taxonomies out of the class structureMove most of your taxonomies out of the class structureMove most of your taxonomies out of the class structureMove most of your taxonomies out of the class structure •Separation of governanceSeparation of governanceSeparation of governanceSeparation of governance •Simplification of the modelSimplification of the modelSimplification of the modelSimplification of the model © 2015 Semantic Arts, Inc. 46
  47. 47. ՞쵰 Move Minor Distinctions to TaxonomiesMove Minor Distinctions to TaxonomiesMove Minor Distinctions to TaxonomiesMove Minor Distinctions to Taxonomies • We’re tempted to putWe’re tempted to putWe’re tempted to putWe’re tempted to put everything we know intoeverything we know intoeverything we know intoeverything we know into our ontologiesour ontologiesour ontologiesour ontologies • Many distinctions areMany distinctions areMany distinctions areMany distinctions are best “pushed” tobest “pushed” tobest “pushed” tobest “pushed” to taxonomiestaxonomiestaxonomiestaxonomies © 2015 Semantic Arts, Inc. taxonomiestaxonomiestaxonomiestaxonomies • Where mere mortals canWhere mere mortals canWhere mere mortals canWhere mere mortals can debate and rearrangedebate and rearrangedebate and rearrangedebate and rearrange themthemthemthem • Without destabilizing theWithout destabilizing theWithout destabilizing theWithout destabilizing the ontologyontologyontologyontology
  48. 48. Ք︀ Should it be in the taxonomy?Should it be in the taxonomy?Should it be in the taxonomy?Should it be in the taxonomy? •Some of the things we consider:Some of the things we consider:Some of the things we consider:Some of the things we consider: oAre there likely to be instances of this thing, or is it more a wayAre there likely to be instances of this thing, or is it more a wayAre there likely to be instances of this thing, or is it more a wayAre there likely to be instances of this thing, or is it more a way to tag or classify instances that already exist?to tag or classify instances that already exist?to tag or classify instances that already exist?to tag or classify instances that already exist? oDo these things have non trivial properties? (trivial propertiesDo these things have non trivial properties? (trivial propertiesDo these things have non trivial properties? (trivial propertiesDo these things have non trivial properties? (trivial properties are things like “synonym” or “are things like “synonym” or “are things like “synonym” or “are things like “synonym” or “broaderThanbroaderThanbroaderThanbroaderThan” or” or” or” or © 2015 Semantic Arts, Inc. are things like “synonym” or “are things like “synonym” or “are things like “synonym” or “are things like “synonym” or “broaderThanbroaderThanbroaderThanbroaderThan” or” or” or” or ““““expressedInLanguageexpressedInLanguageexpressedInLanguageexpressedInLanguage”, non trivial properties include things like”, non trivial properties include things like”, non trivial properties include things like”, non trivial properties include things like “owns” “governs” ““owns” “governs” ““owns” “governs” ““owns” “governs” “isPartyToisPartyToisPartyToisPartyTo”)”)”)”) 48
  49. 49. Ք︀ Classes and Taxonomic InstancesClasses and Taxonomic InstancesClasses and Taxonomic InstancesClasses and Taxonomic Instances •Strive to be more likeStrive to be more likeStrive to be more likeStrive to be more like GeoNamesGeoNamesGeoNamesGeoNames thanthanthanthan SnomedSnomedSnomedSnomed GeoNames Snomed Concepts 10 million geographical Presumably millions © 2015 Semantic Arts, Inc. 49 Concepts 10 million geographical names Presumably millions Classes 19 303,035 Properties 33 152 Taxonomic Categories 645 “feature codes” In classes
  50. 50. Ք︀ ModularityModularityModularityModularity –––– a Two Edged Sworda Two Edged Sworda Two Edged Sworda Two Edged Sword Allows multiple © 2015 Semantic Arts, Inc. 50 Allows multiple groups to work independently Allows multiple groups create inconsistencies
  51. 51. Ք쭠 Rules for working with modulesRules for working with modulesRules for working with modulesRules for working with modules •Don’t add disjoints for classes you importDon’t add disjoints for classes you importDon’t add disjoints for classes you importDon’t add disjoints for classes you import •Don’t make a class you import an explicit subclass of otherDon’t make a class you import an explicit subclass of otherDon’t make a class you import an explicit subclass of otherDon’t make a class you import an explicit subclass of other classes (yours or others)classes (yours or others)classes (yours or others)classes (yours or others) •Don’t change the definition of classes or properties youDon’t change the definition of classes or properties youDon’t change the definition of classes or properties youDon’t change the definition of classes or properties you © 2015 Semantic Arts, Inc. •Don’t change the definition of classes or properties youDon’t change the definition of classes or properties youDon’t change the definition of classes or properties youDon’t change the definition of classes or properties you importimportimportimport •It’s ok, even encouraged to:It’s ok, even encouraged to:It’s ok, even encouraged to:It’s ok, even encouraged to: oCreate sub classes of classes youCreate sub classes of classes youCreate sub classes of classes youCreate sub classes of classes you importimportimportimport oUse the classes and properties you import in the definition ofUse the classes and properties you import in the definition ofUse the classes and properties you import in the definition ofUse the classes and properties you import in the definition of classes in your moduleclasses in your moduleclasses in your moduleclasses in your module oUse imported classes in Boolean class expressionsUse imported classes in Boolean class expressionsUse imported classes in Boolean class expressionsUse imported classes in Boolean class expressions 51
  52. 52. Ք쭠 Namespaces and ModulesNamespaces and ModulesNamespaces and ModulesNamespaces and Modules •Many ontologies use many namespaces (most of the thirdMany ontologies use many namespaces (most of the thirdMany ontologies use many namespaces (most of the thirdMany ontologies use many namespaces (most of the third party ontologies you inherit have their own namespacesparty ontologies you inherit have their own namespacesparty ontologies you inherit have their own namespacesparty ontologies you inherit have their own namespaces –––– skosskosskosskos:,:,:,:, foaffoaffoaffoaf:,:,:,:, dbpediadbpediadbpediadbpedia:::: etcetcetcetc)))) •There is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology have © 2015 Semantic Arts, Inc. •There is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology haveThere is a temptation to have each module/ontology have its own name spaceits own name spaceits own name spaceits own name space •Minus:Minus:Minus:Minus: o Makes moving concepts from module to module difficult (they have to be renamed and allMakes moving concepts from module to module difficult (they have to be renamed and allMakes moving concepts from module to module difficult (they have to be renamed and allMakes moving concepts from module to module difficult (they have to be renamed and all the references to them changed)the references to them changed)the references to them changed)the references to them changed) o It’s a added burden on the consumer to know the namespace for each conceptIt’s a added burden on the consumer to know the namespace for each conceptIt’s a added burden on the consumer to know the namespace for each conceptIt’s a added burden on the consumer to know the namespace for each concept •Plus:Plus:Plus:Plus: o It allows each module to coin there own terms without central coordinationIt allows each module to coin there own terms without central coordinationIt allows each module to coin there own terms without central coordinationIt allows each module to coin there own terms without central coordination 52
  53. 53. Ք쭠 Namespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our Recommendation •Think about the scope of a governance group that isThink about the scope of a governance group that isThink about the scope of a governance group that isThink about the scope of a governance group that is tasked with creating and naming conceptstasked with creating and naming conceptstasked with creating and naming conceptstasked with creating and naming concepts •This will be the broadest a name space should be (if theThis will be the broadest a name space should be (if theThis will be the broadest a name space should be (if theThis will be the broadest a name space should be (if the namespaces were broader, every new term would have tonamespaces were broader, every new term would have tonamespaces were broader, every new term would have tonamespaces were broader, every new term would have to © 2015 Semantic Arts, Inc. namespaces were broader, every new term would have tonamespaces were broader, every new term would have tonamespaces were broader, every new term would have tonamespaces were broader, every new term would have to be reviewed with other groups to ensure consistency andbe reviewed with other groups to ensure consistency andbe reviewed with other groups to ensure consistency andbe reviewed with other groups to ensure consistency and prevent name collision)prevent name collision)prevent name collision)prevent name collision) 53 us:Account us:Account CRM Governance Group Finance Governance Group crm:Account fin:Account CRM Governance Group Finance Governance Group
  54. 54. Ք쭠 Namespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our RecommendationNamespaces: Our Recommendation •But don’t over do this.But don’t over do this.But don’t over do this.But don’t over do this. •If you create a namespace for every (modular) ontology,If you create a namespace for every (modular) ontology,If you create a namespace for every (modular) ontology,If you create a namespace for every (modular) ontology, you will create confusion, unnecessary duplication and makeyou will create confusion, unnecessary duplication and makeyou will create confusion, unnecessary duplication and makeyou will create confusion, unnecessary duplication and make refactoring more difficultrefactoring more difficultrefactoring more difficultrefactoring more difficult •Have one namespace over as many ontologies as yourHave one namespace over as many ontologies as yourHave one namespace over as many ontologies as yourHave one namespace over as many ontologies as your © 2015 Semantic Arts, Inc. •Have one namespace over as many ontologies as yourHave one namespace over as many ontologies as yourHave one namespace over as many ontologies as yourHave one namespace over as many ontologies as your governance can managegovernance can managegovernance can managegovernance can manage 54 sales:Prospect CRM Governance Group mkt:Lead in:Customer ps:Client crm:Prospect CRM Governance Group crm:Lead crm:Customer crm:Client
  55. 55. Ք쭠 Separation of ConcernsSeparation of ConcernsSeparation of ConcernsSeparation of Concerns •Meaning (OWL) Structure (RDF Shapes)Meaning (OWL) Structure (RDF Shapes)Meaning (OWL) Structure (RDF Shapes)Meaning (OWL) Structure (RDF Shapes) •If you’re trying to answer the question “which properties goIf you’re trying to answer the question “which properties goIf you’re trying to answer the question “which properties goIf you’re trying to answer the question “which properties go on this class” by looking at the ontology, you’ve collapsedon this class” by looking at the ontology, you’ve collapsedon this class” by looking at the ontology, you’ve collapsedon this class” by looking at the ontology, you’ve collapsed these two ideasthese two ideasthese two ideasthese two ideas © 2015 Semantic Arts, Inc. these two ideasthese two ideasthese two ideasthese two ideas •The ontology should be the province of coining new terms,The ontology should be the province of coining new terms,The ontology should be the province of coining new terms,The ontology should be the province of coining new terms, establishing meaning and providing the rules for inferenceestablishing meaning and providing the rules for inferenceestablishing meaning and providing the rules for inferenceestablishing meaning and providing the rules for inference 55
  56. 56. Ք쭠 RDF Shape ExampleRDF Shape ExampleRDF Shape ExampleRDF Shape Example <ProductRef> { rdf:type (spo:ProductReference) , gist:identifiedBy @<ProductID> , spo:describedBy @<ProductReferenceDescription> ? , gist:memberOf (@<ProductRange> | @<ProductSubRange> © 2015 Semantic Arts, Inc. 56 , gist:memberOf (@<ProductRange> | @<ProductSubRange> |@<ProductSubSubRange> |@<NewProductRange>) * , gist:categorizedBy @< ProductOrComponentType> * , gist:categorizedBy @< ProductFunction> * , gist:conformsTo @<Standard> * , gist:specifiedBy (@<SpecEntry> |@<TabularSpecEntry>)? }
  57. 57. Ք쭠 SummarySummarySummarySummary •It is necessary (and luckily possible) to create enterpriseIt is necessary (and luckily possible) to create enterpriseIt is necessary (and luckily possible) to create enterpriseIt is necessary (and luckily possible) to create enterprise ontologiesontologiesontologiesontologies in a relatively short time frame that can bein a relatively short time frame that can bein a relatively short time frame that can bein a relatively short time frame that can be evolved in placeevolved in placeevolved in placeevolved in place •There are many techniques that aid in that effort:There are many techniques that aid in that effort:There are many techniques that aid in that effort:There are many techniques that aid in that effort: oShift from discovery to postulationShift from discovery to postulationShift from discovery to postulationShift from discovery to postulation © 2015 Semantic Arts, Inc. oShift from discovery to postulationShift from discovery to postulationShift from discovery to postulationShift from discovery to postulation oReduce the cognitive load for the consumer of the ontologyReduce the cognitive load for the consumer of the ontologyReduce the cognitive load for the consumer of the ontologyReduce the cognitive load for the consumer of the ontology oModularize and separate taxonomies and instances to makeModularize and separate taxonomies and instances to makeModularize and separate taxonomies and instances to makeModularize and separate taxonomies and instances to make maintenance easiermaintenance easiermaintenance easiermaintenance easier oTest reasoning and results frequentlyTest reasoning and results frequentlyTest reasoning and results frequentlyTest reasoning and results frequently oWhen modularizing, set up some rules to keep people fromWhen modularizing, set up some rules to keep people fromWhen modularizing, set up some rules to keep people fromWhen modularizing, set up some rules to keep people from clobbering each otherclobbering each otherclobbering each otherclobbering each other oKeep structure out of the ontologyKeep structure out of the ontologyKeep structure out of the ontologyKeep structure out of the ontology 57
  58. 58. Ք쭠 Question TimeQuestion TimeQuestion TimeQuestion Time •To pursue this further:To pursue this further:To pursue this further:To pursue this further: oArticles and blogsArticles and blogsArticles and blogsArticles and blogs http://www.semanticarts.com oGist (free!)Gist (free!)Gist (free!)Gist (free!) http://www.semanticarts.com/gist © 2015 Semantic Arts, Inc. http://www.semanticarts.com/gist oData Centric Manifesto (become a signatory)Data Centric Manifesto (become a signatory)Data Centric Manifesto (become a signatory)Data Centric Manifesto (become a signatory) http://datacentricmanifesto.org oContact me (Dave McComb)Contact me (Dave McComb)Contact me (Dave McComb)Contact me (Dave McComb) mccomb@semanticarts.com (970) 490-2224 58

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