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Squashing and Squeezing:
Extracting Operational ontologies (and data
models) from Concept Ontology
A Semantic Shed Explanatory Deck
April 2024
Overview Framework:
Concept v Data Ontologies
Ontology Referents: Real World
The Language
Interface
Business
Technology
OWL Serialization of DL Model
OWL is a serialization of Description Logic
• Referent is things in the real world
Concept Level
Physical level (data)
DL Model
represents
Things in the World
Strictly: What we believe exists
Logical (design) level
represents
Serialize
RDF Instance data
(Knowledge Graph)
represents
Ontology Referents: RDF Data
The Language
Interface
Business
Technology
OWL Serialization of DL Model
Concept Level
Physical level (data)
DL Model
represents
Things in the World
Strictly: What we believe exists
represents
Logical (design) level
represents
Serialize
OWL accompanies instance data
Referent switches:
• from real world referent
• to data representing those things
RDF Instance data
(Knowledge Graph)
Introduce a Data Ontology
The Language
Interface
Business
Technology
OWL Serialization of DL Model
How useful is this ontology as a KB schema?
• Needs datatypes
• Some real things may not be reflected in
the data world at all
• D
• Data
Concept Ontology
Data
DL Model
represents
Data Ontology
represents
Serialize
Things in the World
Strictly: What we believe exists
Conceptual Data Ontology
RDF Instance data
(Knowledge Graph)
Introduce a Data Ontology
The Language
Interface
Business
Technology
OWL Serialization of DL Model
How useful is this ontology as a KB schema?
• Needs datatypes
• Some real things may not be reflected in
the data world at all
• D
• Data
Concept Ontology
Data
DL Model
represents
Data Ontology
represents
Serialize
Things in the World
Strictly: What we believe exists
Conceptual Data Ontology
RDF Instance data
(Knowledge Graph)
Can also think of this as a
“Semantic Data Model”
Add Datatypes for RDF Data
The Language
Interface
Business
Technology
OWL Serialization of DL Model
Conceptual Data Ontology:
• Add RDF/XML datatypes
• D
• Data
Concept Ontology
Data
DL Model
represents
Data Ontology
represents
Serialize
Things in the World
Strictly: What we believe exists
Conceptual Data Ontology
Datatypes
Triple store data
RDF Instance data
(Knowledge Graph)
Conceptual Data Ontology
Add Data Surrogates for non-Data Items
The Language
Interface
Business
Technology
OWL Serialization of DL Model
Some real things may not be reflected in
the data world at all
• Add Data surrogates
• D
• Data
Concept Ontology
Data
DL Model
Datatypes
Data
Surrogates
represents
Data Ontology
Includes data surrogates
Triple store data
represents
Serialize
Things in the World
Strictly: What we believe exists
RDF Instance data
(Knowledge Graph)
Ontology Types and Styles
Concept v Data Ontologies
Ontology
Concept
Ontology
Data
Ontology
Reference v Application Ontologies
Reference
requirement
Use Case
To be the kind of thing
that has a use case is to
be an application!
Reference
Ontology
Application
Ontology
Ontology
Concept
Ontology
Data
Ontology
Use Concept Ontology
as a point of reference
for business meanings
AKA
Explanatory
Ontology
Conceptual Data Ontology
Ontology
Concept
Ontology
Data
Ontology
Conceptual
Data
Ontology
Inferencing
Application
Ontology
Mapping
requirement
Knowledge
Graph
requirement
Reasoning / Inference Processing Ontology
Ontology
Concept
Ontology
Data
Ontology
Conceptual
Data
Ontology
Inferencing
Application
Ontology
Mapped Data
Taxonomy of Ontologies
Ontology
Concept
Ontology
Data
Ontology
Conceptual
Data
Ontology
Inferencing
Application
Ontology
Knowledge Graph RDF App Data
Mapped Data
Mapped Data
Operational Ontology Extraction
Extraction and Design Techniques
• We will look at two techniques:
• Squashing (Telescoping)
• Applicable to any Concept v Data ontology transformation
• Squeezing (Concertinaing)
• more applicable to application ontology than to data-facing KG Schema / Conceptual
Data Ontology
• Application considerations:
• Need to extract and simplify from the conceptual representation of things, to
what’s appropriate for data processing
• No need for Top Level Ontology (TLO) in application ontology
• Use Ontology Design Patterns (ODP) in place of direct inheritance from TLO
A Tale of Two Post Codes
• There’s a story that’s told…
• About a bank with a book of mortgage loans.
• They wanted to see if there was any concentration risk in the
mortgage portfolio
• So they looked at the addresses:
Risk Concentration: Apparent
But that wasn’t the full story
• Most of these mortgages were for second homes
• The addresses they were looking at were for the borrowers.
• Here’s where the houses were…
Risk Concentration: Actual
Story2
Oops!
• There was a semantic distinction to be made between two things:
• The risk represented by the borrower’s address
• The risk represented by the homes used as mortgage collateral
• These are two different meanings
• So we put these in an ontology (for real world meanings)
The Problem
• There are multiple uses of the concept of “conventional street
address”
• Each use has a different meaning
• The address of a mortgage borrower (e.g., living in New York)
• The address of the collateral (e.g., in Florida)
• Each use is defined by a unique path from Mortgage
Telescoping
Inherited properties
property a
property b
property c
property d
Class A
property e
property f
Class B
property g
property h
property j
Class C
property k
Class D Distinguishing feature(s)
• Concept Model / Ontology
has mandatory properties at
each level where they apply
Telescoping
• Concept Model / Ontology
has mandatory properties at
each level where they apply
property a
property b
property c
property d
Class A
property e
property f
Class B
property g
property h
property j
Class C
Class D
property k
Telescoping
• Concept Model / Ontology has mandatory properties at each level where they apply
• For data model we want one class with all the (inherited) properties and other
expressions (restrictions etc.)
• So we want to ‘squash’ (telescope) the hierarchy, retaining cardinalities as appropriate
property a
property b
property c
property d
Class D
property e
property f
property g
property h
property j
property k
Squashing
Squashing
Squeezing: Concertina Effect
• Concept Model has lots of high-level distinctions:
• Things in themselves versus things in roles (e.g. collateral, party)
• Geographical versus geophysical
• Things versus records / data about things
• Etc.
• We need to squeeze these together as needed for a given data model
Class Class Class Class
property property property
Squeezing: Concertina Effect
• Concept Model has lots of high-level distinctions:
• Things in themselves versus things in roles (e.g. collateral, party)
• Geographical versus geophysical
• Things versus records / data about things
• Etc.
• We need to squeeze these together as needed for a given data model
Class Class
property
Squeezing: The Multi-Path Problem
Loan Borrower Address
Collateral Address
Path to Mortgage Loan Borrower Address
Loan Borrower Address
Mortgage Loan Borrower Address Definition
Credit: Jim Logan
Mortgage Loan Borrower Address Refinement
• In description logic:
• ‘Mortgage Loan Borrower Address’ ≡ ‘conventional street address’ ⊓ ∃‘has
residence’-.(‘legally competent natural person’ ⊓ ∃’has identity’-.(‘borrower’
⊓ ∃’has borrower’-.’Mortgage Loan Contract’))
• In description logic, after naming inverse properties:
• ‘Mortgage Loan Borrower Address’ ≡ ‘conventional street address’ ⊓
∃‘residence of’.(‘legally competent natural person’ ⊓ ∃’identity
of’.(‘borrower’ ⊓ ∃’borrower in’.’Mortgage Loan Contract’))
• In plain English:
• exactly a conventional street address that is the residence of exactly some
legally competent natural person that is the identity of exactly some borrower
that is the borrower in a mortgage loan contract
• In a CCM diagram (next slide)
Credit: Jim Logan
Mortgage Loan Borrower Address: Option One
• Same namespace
Credit: Jim Logan
Mortgage Loan Borrower Address: Option Two
Mortgage Loan Contract Conventional
Street Address
Thing has borrower primary residence address
on property
must be some
• Option Two: Operational Ontology in separate namespace
Borrower Risk
Information
(Data) Model
Path to Collateral Address
Collateral Address
Collateral Address Definition
Credit: Jim Logan
Collateral Address Refinement
• In description logic:
• 'Collateral Address' ≡ 'conventional street address' ⊓ ∃'has address'-.('real
estate' ⊓ ∃'has identity'-.(Real Estate Collateral ⊓ ∃'is collateralized by'-
.('Mortgage Loan Contract’)))
• In description logic, after naming inverse properties:
• 'Collateral Address' ≡ 'conventional street address' ⊓ ∃'address of'.('real
estate' ⊓ ∃'identity of'.(Real Estate Collateral ⊓ ∃'collateralizes'.('Mortgage
Loan Contract’)))
• In plain English:
• exactly a conventional street address that is the address of exactly some real
estate that is the identity of exactly some real estate collateral that
collateralizes a mortgage loan contract
• In a CCM diagram (next slide)
Credit: Jim Logan
Collateral Address: Option One
• Same namespace
Credit: Jim Logan
Collateral Address: Option Two
Mortgage Loan Contract Physical Address
Thing has collateral address
on property
may only be
Conventional Street
Address
• Option Two: Operational Ontology in separate namespace
Collateral Risk Information (Data) Model
Summary: Squashing and Squeezing
Credit: Jim Logan
Thank you!
The Semantic Shed
www.semanticshed.org
© The Semantic Shed, 2024
© The Carmarthen Group Ltd, 2024

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Semantic Shed - Squashing and Squeezing.pptx

  • 1. Squashing and Squeezing: Extracting Operational ontologies (and data models) from Concept Ontology A Semantic Shed Explanatory Deck April 2024
  • 3. Ontology Referents: Real World The Language Interface Business Technology OWL Serialization of DL Model OWL is a serialization of Description Logic • Referent is things in the real world Concept Level Physical level (data) DL Model represents Things in the World Strictly: What we believe exists Logical (design) level represents Serialize RDF Instance data (Knowledge Graph) represents
  • 4. Ontology Referents: RDF Data The Language Interface Business Technology OWL Serialization of DL Model Concept Level Physical level (data) DL Model represents Things in the World Strictly: What we believe exists represents Logical (design) level represents Serialize OWL accompanies instance data Referent switches: • from real world referent • to data representing those things RDF Instance data (Knowledge Graph)
  • 5. Introduce a Data Ontology The Language Interface Business Technology OWL Serialization of DL Model How useful is this ontology as a KB schema? • Needs datatypes • Some real things may not be reflected in the data world at all • D • Data Concept Ontology Data DL Model represents Data Ontology represents Serialize Things in the World Strictly: What we believe exists Conceptual Data Ontology RDF Instance data (Knowledge Graph)
  • 6. Introduce a Data Ontology The Language Interface Business Technology OWL Serialization of DL Model How useful is this ontology as a KB schema? • Needs datatypes • Some real things may not be reflected in the data world at all • D • Data Concept Ontology Data DL Model represents Data Ontology represents Serialize Things in the World Strictly: What we believe exists Conceptual Data Ontology RDF Instance data (Knowledge Graph) Can also think of this as a “Semantic Data Model”
  • 7. Add Datatypes for RDF Data The Language Interface Business Technology OWL Serialization of DL Model Conceptual Data Ontology: • Add RDF/XML datatypes • D • Data Concept Ontology Data DL Model represents Data Ontology represents Serialize Things in the World Strictly: What we believe exists Conceptual Data Ontology Datatypes Triple store data RDF Instance data (Knowledge Graph)
  • 8. Conceptual Data Ontology Add Data Surrogates for non-Data Items The Language Interface Business Technology OWL Serialization of DL Model Some real things may not be reflected in the data world at all • Add Data surrogates • D • Data Concept Ontology Data DL Model Datatypes Data Surrogates represents Data Ontology Includes data surrogates Triple store data represents Serialize Things in the World Strictly: What we believe exists RDF Instance data (Knowledge Graph)
  • 10. Concept v Data Ontologies Ontology Concept Ontology Data Ontology
  • 11. Reference v Application Ontologies Reference requirement Use Case To be the kind of thing that has a use case is to be an application! Reference Ontology Application Ontology Ontology Concept Ontology Data Ontology Use Concept Ontology as a point of reference for business meanings AKA Explanatory Ontology
  • 13. Reasoning / Inference Processing Ontology Ontology Concept Ontology Data Ontology Conceptual Data Ontology Inferencing Application Ontology
  • 14. Mapped Data Taxonomy of Ontologies Ontology Concept Ontology Data Ontology Conceptual Data Ontology Inferencing Application Ontology Knowledge Graph RDF App Data Mapped Data Mapped Data
  • 16. Extraction and Design Techniques • We will look at two techniques: • Squashing (Telescoping) • Applicable to any Concept v Data ontology transformation • Squeezing (Concertinaing) • more applicable to application ontology than to data-facing KG Schema / Conceptual Data Ontology • Application considerations: • Need to extract and simplify from the conceptual representation of things, to what’s appropriate for data processing • No need for Top Level Ontology (TLO) in application ontology • Use Ontology Design Patterns (ODP) in place of direct inheritance from TLO
  • 17. A Tale of Two Post Codes • There’s a story that’s told… • About a bank with a book of mortgage loans. • They wanted to see if there was any concentration risk in the mortgage portfolio • So they looked at the addresses:
  • 19. But that wasn’t the full story • Most of these mortgages were for second homes • The addresses they were looking at were for the borrowers. • Here’s where the houses were…
  • 22. Oops! • There was a semantic distinction to be made between two things: • The risk represented by the borrower’s address • The risk represented by the homes used as mortgage collateral • These are two different meanings • So we put these in an ontology (for real world meanings)
  • 23. The Problem • There are multiple uses of the concept of “conventional street address” • Each use has a different meaning • The address of a mortgage borrower (e.g., living in New York) • The address of the collateral (e.g., in Florida) • Each use is defined by a unique path from Mortgage
  • 24. Telescoping Inherited properties property a property b property c property d Class A property e property f Class B property g property h property j Class C property k Class D Distinguishing feature(s) • Concept Model / Ontology has mandatory properties at each level where they apply
  • 25. Telescoping • Concept Model / Ontology has mandatory properties at each level where they apply property a property b property c property d Class A property e property f Class B property g property h property j Class C Class D property k
  • 26. Telescoping • Concept Model / Ontology has mandatory properties at each level where they apply • For data model we want one class with all the (inherited) properties and other expressions (restrictions etc.) • So we want to ‘squash’ (telescope) the hierarchy, retaining cardinalities as appropriate property a property b property c property d Class D property e property f property g property h property j property k
  • 29. Squeezing: Concertina Effect • Concept Model has lots of high-level distinctions: • Things in themselves versus things in roles (e.g. collateral, party) • Geographical versus geophysical • Things versus records / data about things • Etc. • We need to squeeze these together as needed for a given data model Class Class Class Class property property property
  • 30. Squeezing: Concertina Effect • Concept Model has lots of high-level distinctions: • Things in themselves versus things in roles (e.g. collateral, party) • Geographical versus geophysical • Things versus records / data about things • Etc. • We need to squeeze these together as needed for a given data model Class Class property
  • 31. Squeezing: The Multi-Path Problem Loan Borrower Address Collateral Address
  • 32. Path to Mortgage Loan Borrower Address Loan Borrower Address
  • 33. Mortgage Loan Borrower Address Definition Credit: Jim Logan
  • 34. Mortgage Loan Borrower Address Refinement • In description logic: • ‘Mortgage Loan Borrower Address’ ≡ ‘conventional street address’ ⊓ ∃‘has residence’-.(‘legally competent natural person’ ⊓ ∃’has identity’-.(‘borrower’ ⊓ ∃’has borrower’-.’Mortgage Loan Contract’)) • In description logic, after naming inverse properties: • ‘Mortgage Loan Borrower Address’ ≡ ‘conventional street address’ ⊓ ∃‘residence of’.(‘legally competent natural person’ ⊓ ∃’identity of’.(‘borrower’ ⊓ ∃’borrower in’.’Mortgage Loan Contract’)) • In plain English: • exactly a conventional street address that is the residence of exactly some legally competent natural person that is the identity of exactly some borrower that is the borrower in a mortgage loan contract • In a CCM diagram (next slide) Credit: Jim Logan
  • 35. Mortgage Loan Borrower Address: Option One • Same namespace Credit: Jim Logan
  • 36. Mortgage Loan Borrower Address: Option Two Mortgage Loan Contract Conventional Street Address Thing has borrower primary residence address on property must be some • Option Two: Operational Ontology in separate namespace
  • 38. Path to Collateral Address Collateral Address
  • 40. Collateral Address Refinement • In description logic: • 'Collateral Address' ≡ 'conventional street address' ⊓ ∃'has address'-.('real estate' ⊓ ∃'has identity'-.(Real Estate Collateral ⊓ ∃'is collateralized by'- .('Mortgage Loan Contract’))) • In description logic, after naming inverse properties: • 'Collateral Address' ≡ 'conventional street address' ⊓ ∃'address of'.('real estate' ⊓ ∃'identity of'.(Real Estate Collateral ⊓ ∃'collateralizes'.('Mortgage Loan Contract’))) • In plain English: • exactly a conventional street address that is the address of exactly some real estate that is the identity of exactly some real estate collateral that collateralizes a mortgage loan contract • In a CCM diagram (next slide) Credit: Jim Logan
  • 41. Collateral Address: Option One • Same namespace Credit: Jim Logan
  • 42. Collateral Address: Option Two Mortgage Loan Contract Physical Address Thing has collateral address on property may only be Conventional Street Address • Option Two: Operational Ontology in separate namespace
  • 44. Summary: Squashing and Squeezing Credit: Jim Logan
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