SlideShare a Scribd company logo
1 of 9
Making Sense of Design Patterns Rinke Hoekstrahoekstra@few.vu.nl, hoekstra@uva.nlJoostBreukerbreuker@science.uva.nl There must be a reason why certain patterns are more useful than others + BONUS DP!!!
How to build a “Good Ontology” Design principles Distinguish accidental from intrinsic properties Abstract, difficult to apply Reuse of existing ontologies Nice bootstrap, but problematic Large, heavyweight, hard to extend Design patterns Middle ground Principles as concrete building blocks
What’s a good Design Pattern? Categorise Logical, content, lexico-syntactic, ... Submit and Review http://ontologydesignpatterns.org Incentive to share? ... preliminary evaluation results (Blomqvist et al., 2009) Criteria Mix required metadata, with quality criteria Pros and cons, competency questions “cognitively relevant” and “best practices”
Linguistics “Give a muffin to a moose” vs. “Give a moose a muffin” “Biff drove the car to Chicago” vs. “Biff drove Chicago the car” Linguistic expressions follow cognitive rules (Pinker, 2007) Recurring structures in language Can be reapplied to create new meaning Signal fundamental concepts of thought “We gather our ideas, put them into words, and if our verbiage is not empty or hollow, we might get these ideas across to a listener, who can unpack our words to extract their content”
Design Decisions Conceptual Model Two Sides to a Coin Ontology DPs ... KADS, CommonKADS “Knowledge modelling” (van Heijst et al., ‘97) Design patterns bridge the gap they are specific to a KR language ... but commit to a conceptual model that exists independently of it
Fundamental Design Decision Design patterns commit to a conceptualisation express a structure in a language thereby exclude other solutions Well known commitments... Binary vs. n-ary relations (action) Relative vs. absolute (time, place) Reification vs. abstraction (roles) Are roles classes or relations?
Roles BONUS DP!!! Are roles classes or relations? Searle, The Structure of Social Reality, 1995 Rinke Hoekstra. Representing Social Reality in OWL 2. In EvrenSirinand Kendall Clark, ed., Proceedings of OWLED 2010, June 2010
It’s like Legotm!
Discussion Message: move beyond best practices Design patterns Capture fundamental design decisions,  Recurrent structures that reflect cognitive notions Bridge the gap between conceptualization and implementation. Give insight in expert knowledge What next? Domain theories, but also linguistics and cognition Harvest recurring patterns in existing ontologies Assess tradeoffs, i.e. discover design decisions Design patterns as index to a library of ontologies

More Related Content

Similar to Making Sense of Design Patterns

Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative DesignDefense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative DesignRobin Teigland
 
ServDes16 - Thematic Research in the Frame Creation Process
ServDes16 - Thematic Research in the Frame Creation ProcessServDes16 - Thematic Research in the Frame Creation Process
ServDes16 - Thematic Research in the Frame Creation ProcessJos van Leeuwen
 
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...ServDes
 
Knowledge Patterns SSSW2016
Knowledge Patterns SSSW2016Knowledge Patterns SSSW2016
Knowledge Patterns SSSW2016Aldo Gangemi
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyPalGov
 
The wickedness of design research practice - IASDR 2013
The wickedness of design research practice - IASDR 2013The wickedness of design research practice - IASDR 2013
The wickedness of design research practice - IASDR 2013fatech
 
21 Years of Applied Ontology
21 Years of Applied Ontology21 Years of Applied Ontology
21 Years of Applied OntologyNicola Guarino
 
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matter
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matterPHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matter
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matterPeter Jones
 
Models of Co-Creation - De Koning, Crul, Wever
Models of Co-Creation - De Koning, Crul, WeverModels of Co-Creation - De Koning, Crul, Wever
Models of Co-Creation - De Koning, Crul, WeverServDes
 
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter RossingThe role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter RossingJoanna Hicks
 
Discourse analysis (Schmitt's book chapter 4)
Discourse analysis (Schmitt's book chapter 4)Discourse analysis (Schmitt's book chapter 4)
Discourse analysis (Schmitt's book chapter 4)Samira Rahmdel
 
Putting People First at Steelcase
Putting People First at SteelcasePutting People First at Steelcase
Putting People First at SteelcaseHuman Capital Media
 
Knowledge engineering and the Web
Knowledge engineering and the WebKnowledge engineering and the Web
Knowledge engineering and the WebGuus Schreiber
 
Bridging the missing middle for al_tversionfinal_14_08_2014
Bridging the missing middle for al_tversionfinal_14_08_2014Bridging the missing middle for al_tversionfinal_14_08_2014
Bridging the missing middle for al_tversionfinal_14_08_2014debbieholley1
 
Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)evabl444
 
ISWC2023-McGuinnessTWC16x9FinalShort.pdf
ISWC2023-McGuinnessTWC16x9FinalShort.pdfISWC2023-McGuinnessTWC16x9FinalShort.pdf
ISWC2023-McGuinnessTWC16x9FinalShort.pdfDeborah McGuinness
 

Similar to Making Sense of Design Patterns (20)

Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative DesignDefense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
 
Models and Ontologies: differences
Models and Ontologies: differencesModels and Ontologies: differences
Models and Ontologies: differences
 
ServDes16 - Thematic Research in the Frame Creation Process
ServDes16 - Thematic Research in the Frame Creation ProcessServDes16 - Thematic Research in the Frame Creation Process
ServDes16 - Thematic Research in the Frame Creation Process
 
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...
Thematic Research in the Frame Creation Process - Leeuwen, Rijken, Bloothoofd...
 
Knowledge Patterns SSSW2016
Knowledge Patterns SSSW2016Knowledge Patterns SSSW2016
Knowledge Patterns SSSW2016
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
 
The wickedness of design research practice - IASDR 2013
The wickedness of design research practice - IASDR 2013The wickedness of design research practice - IASDR 2013
The wickedness of design research practice - IASDR 2013
 
21 Years of Applied Ontology
21 Years of Applied Ontology21 Years of Applied Ontology
21 Years of Applied Ontology
 
Sicilia-Aera08
Sicilia-Aera08Sicilia-Aera08
Sicilia-Aera08
 
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matter
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matterPHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matter
PHPnw (England) User Group - Concepts, Spaces and Thresholds and why they matter
 
Models of Co-Creation - De Koning, Crul, Wever
Models of Co-Creation - De Koning, Crul, WeverModels of Co-Creation - De Koning, Crul, Wever
Models of Co-Creation - De Koning, Crul, Wever
 
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter RossingThe role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
 
Discourse analysis (Schmitt's book chapter 4)
Discourse analysis (Schmitt's book chapter 4)Discourse analysis (Schmitt's book chapter 4)
Discourse analysis (Schmitt's book chapter 4)
 
Putting People First at Steelcase
Putting People First at SteelcasePutting People First at Steelcase
Putting People First at Steelcase
 
Knowledge engineering and the Web
Knowledge engineering and the WebKnowledge engineering and the Web
Knowledge engineering and the Web
 
Bridging the missing middle for al_tversionfinal_14_08_2014
Bridging the missing middle for al_tversionfinal_14_08_2014Bridging the missing middle for al_tversionfinal_14_08_2014
Bridging the missing middle for al_tversionfinal_14_08_2014
 
Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)
 
ISWC2023-McGuinnessTWC16x9FinalShort.pdf
ISWC2023-McGuinnessTWC16x9FinalShort.pdfISWC2023-McGuinnessTWC16x9FinalShort.pdf
ISWC2023-McGuinnessTWC16x9FinalShort.pdf
 
OU 2011
OU 2011OU 2011
OU 2011
 
The basics of ontologies
The basics of ontologiesThe basics of ontologies
The basics of ontologies
 

More from Rinke Hoekstra

Knowledge Representation on the Web
Knowledge Representation on the WebKnowledge Representation on the Web
Knowledge Representation on the WebRinke Hoekstra
 
Managing Metadata for Science and Technology Studies: the RISIS case
Managing Metadata for Science and Technology Studies: the RISIS caseManaging Metadata for Science and Technology Studies: the RISIS case
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
 
An Ecosystem for Linked Humanities Data
An Ecosystem for Linked Humanities DataAn Ecosystem for Linked Humanities Data
An Ecosystem for Linked Humanities DataRinke Hoekstra
 
QBer - Connect your data to the cloud
QBer - Connect your data to the cloudQBer - Connect your data to the cloud
QBer - Connect your data to the cloudRinke Hoekstra
 
Jurix 2014 welcome presentation
Jurix 2014 welcome presentationJurix 2014 welcome presentation
Jurix 2014 welcome presentationRinke Hoekstra
 
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)Provenance and Reuse of Open Data (PILOD 2.0 June 2014)
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)Rinke Hoekstra
 
Prov-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance VisualizationProv-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance VisualizationRinke Hoekstra
 
Linkitup: Link Discovery for Research Data
Linkitup: Link Discovery for Research DataLinkitup: Link Discovery for Research Data
Linkitup: Link Discovery for Research DataRinke Hoekstra
 
A Network Analysis of Dutch Regulations - Using the Metalex Document Server
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerA Network Analysis of Dutch Regulations - Using the Metalex Document Server
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerRinke Hoekstra
 
Linked (Open) Data - But what does it buy me?
Linked (Open) Data - But what does it buy me?Linked (Open) Data - But what does it buy me?
Linked (Open) Data - But what does it buy me?Rinke Hoekstra
 
Linked Science - Building a Web of Research Data
Linked Science - Building a Web of Research DataLinked Science - Building a Web of Research Data
Linked Science - Building a Web of Research DataRinke Hoekstra
 
Semantic Representations for Research
Semantic Representations for ResearchSemantic Representations for Research
Semantic Representations for ResearchRinke Hoekstra
 
A Slightly Different Web of Data
A Slightly Different Web of DataA Slightly Different Web of Data
A Slightly Different Web of DataRinke Hoekstra
 
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering BottleneckThe Knowledge Reengineering Bottleneck
The Knowledge Reengineering BottleneckRinke Hoekstra
 
Concept- en Definitie Extractie
Concept- en Definitie ExtractieConcept- en Definitie Extractie
Concept- en Definitie ExtractieRinke Hoekstra
 
SIKS 2011 Semantic Web Languages
SIKS 2011 Semantic Web LanguagesSIKS 2011 Semantic Web Languages
SIKS 2011 Semantic Web LanguagesRinke Hoekstra
 
The MetaLex Document Server - Legal Documents as Versioned Linked Data
The MetaLex Document Server - Legal Documents as Versioned Linked DataThe MetaLex Document Server - Legal Documents as Versioned Linked Data
The MetaLex Document Server - Legal Documents as Versioned Linked DataRinke Hoekstra
 
Querying the Web of Data
Querying the Web of DataQuerying the Web of Data
Querying the Web of DataRinke Hoekstra
 

More from Rinke Hoekstra (20)

Knowledge Representation on the Web
Knowledge Representation on the WebKnowledge Representation on the Web
Knowledge Representation on the Web
 
Managing Metadata for Science and Technology Studies: the RISIS case
Managing Metadata for Science and Technology Studies: the RISIS caseManaging Metadata for Science and Technology Studies: the RISIS case
Managing Metadata for Science and Technology Studies: the RISIS case
 
An Ecosystem for Linked Humanities Data
An Ecosystem for Linked Humanities DataAn Ecosystem for Linked Humanities Data
An Ecosystem for Linked Humanities Data
 
QBer - Connect your data to the cloud
QBer - Connect your data to the cloudQBer - Connect your data to the cloud
QBer - Connect your data to the cloud
 
Jurix 2014 welcome presentation
Jurix 2014 welcome presentationJurix 2014 welcome presentation
Jurix 2014 welcome presentation
 
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)Provenance and Reuse of Open Data (PILOD 2.0 June 2014)
Provenance and Reuse of Open Data (PILOD 2.0 June 2014)
 
Prov-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance VisualizationProv-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance Visualization
 
Linkitup: Link Discovery for Research Data
Linkitup: Link Discovery for Research DataLinkitup: Link Discovery for Research Data
Linkitup: Link Discovery for Research Data
 
A Network Analysis of Dutch Regulations - Using the Metalex Document Server
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerA Network Analysis of Dutch Regulations - Using the Metalex Document Server
A Network Analysis of Dutch Regulations - Using the Metalex Document Server
 
Linked (Open) Data - But what does it buy me?
Linked (Open) Data - But what does it buy me?Linked (Open) Data - But what does it buy me?
Linked (Open) Data - But what does it buy me?
 
Linked Science - Building a Web of Research Data
Linked Science - Building a Web of Research DataLinked Science - Building a Web of Research Data
Linked Science - Building a Web of Research Data
 
COMMIT/VIVO
COMMIT/VIVOCOMMIT/VIVO
COMMIT/VIVO
 
Semantic Representations for Research
Semantic Representations for ResearchSemantic Representations for Research
Semantic Representations for Research
 
A Slightly Different Web of Data
A Slightly Different Web of DataA Slightly Different Web of Data
A Slightly Different Web of Data
 
The Knowledge Reengineering Bottleneck
The Knowledge Reengineering BottleneckThe Knowledge Reengineering Bottleneck
The Knowledge Reengineering Bottleneck
 
Linked Census Data
Linked Census DataLinked Census Data
Linked Census Data
 
Concept- en Definitie Extractie
Concept- en Definitie ExtractieConcept- en Definitie Extractie
Concept- en Definitie Extractie
 
SIKS 2011 Semantic Web Languages
SIKS 2011 Semantic Web LanguagesSIKS 2011 Semantic Web Languages
SIKS 2011 Semantic Web Languages
 
The MetaLex Document Server - Legal Documents as Versioned Linked Data
The MetaLex Document Server - Legal Documents as Versioned Linked DataThe MetaLex Document Server - Legal Documents as Versioned Linked Data
The MetaLex Document Server - Legal Documents as Versioned Linked Data
 
Querying the Web of Data
Querying the Web of DataQuerying the Web of Data
Querying the Web of Data
 

Making Sense of Design Patterns

  • 1. Making Sense of Design Patterns Rinke Hoekstrahoekstra@few.vu.nl, hoekstra@uva.nlJoostBreukerbreuker@science.uva.nl There must be a reason why certain patterns are more useful than others + BONUS DP!!!
  • 2. How to build a “Good Ontology” Design principles Distinguish accidental from intrinsic properties Abstract, difficult to apply Reuse of existing ontologies Nice bootstrap, but problematic Large, heavyweight, hard to extend Design patterns Middle ground Principles as concrete building blocks
  • 3. What’s a good Design Pattern? Categorise Logical, content, lexico-syntactic, ... Submit and Review http://ontologydesignpatterns.org Incentive to share? ... preliminary evaluation results (Blomqvist et al., 2009) Criteria Mix required metadata, with quality criteria Pros and cons, competency questions “cognitively relevant” and “best practices”
  • 4. Linguistics “Give a muffin to a moose” vs. “Give a moose a muffin” “Biff drove the car to Chicago” vs. “Biff drove Chicago the car” Linguistic expressions follow cognitive rules (Pinker, 2007) Recurring structures in language Can be reapplied to create new meaning Signal fundamental concepts of thought “We gather our ideas, put them into words, and if our verbiage is not empty or hollow, we might get these ideas across to a listener, who can unpack our words to extract their content”
  • 5. Design Decisions Conceptual Model Two Sides to a Coin Ontology DPs ... KADS, CommonKADS “Knowledge modelling” (van Heijst et al., ‘97) Design patterns bridge the gap they are specific to a KR language ... but commit to a conceptual model that exists independently of it
  • 6. Fundamental Design Decision Design patterns commit to a conceptualisation express a structure in a language thereby exclude other solutions Well known commitments... Binary vs. n-ary relations (action) Relative vs. absolute (time, place) Reification vs. abstraction (roles) Are roles classes or relations?
  • 7. Roles BONUS DP!!! Are roles classes or relations? Searle, The Structure of Social Reality, 1995 Rinke Hoekstra. Representing Social Reality in OWL 2. In EvrenSirinand Kendall Clark, ed., Proceedings of OWLED 2010, June 2010
  • 9. Discussion Message: move beyond best practices Design patterns Capture fundamental design decisions, Recurrent structures that reflect cognitive notions Bridge the gap between conceptualization and implementation. Give insight in expert knowledge What next? Domain theories, but also linguistics and cognition Harvest recurring patterns in existing ontologies Assess tradeoffs, i.e. discover design decisions Design patterns as index to a library of ontologies
  • 10. There’s more in the paper... Five requirements for design patterns Structure patterns ... much more detail Rinke Hoekstrahoekstra@few.vu.nl / hoekstra@uva.nl There’s also a book  Rinke Hoekstra.Ontology Representation – Design Patterns and Ontologies that Make Sense. IOS Press, 2009