1. Mike Bennett FOIS – JOWO Cape Town, South Africa September 2018
Hypercube Ltd.
Conceptual Ontology Engineering Tutorial Session 3: The Data Dimension; Top Level Ontologies
Mike Bennett
FOIS – JOWO
Cape Town, South Africa
September 2018

2. Session 3 The Data Dimension
Introducing Top Level ontology (Upper Ontology)
Some popular TLOs

3. The Data Dimension Introducing data to ontology Things versus Strings
Truth makers and data surrogates
What about real things that are data?
Information kinds versus datatypes
Values modeling

4. Introducing Data to Ontology
Things
Information
Type
A set specification for a kind of Independent Thing that generalizes all towers (e.g., “a tall narrow structure”)
A set specification for a kind of Dependent Continuant that is a record structure containing tower observations (e.g., a “TOWER” table or a “#Tower” class)
Sets
One of many sets of independent things that generalize all towers
One of many sets of dependent continuant record structures containing tower observations (e.g., in that database there)
Member
A member of zero or more sets of all towers (E.g., the actual one we call the “Eiffel Tower”)
A member of one or more sets of record structures containing tower observations (E.g., one that represents the actual Eiffel Tower)
“#tower123”
Represents
Jim Logan, NoMagic

5. Data surrogate versus Real Things
Look for signatures in data that imply the presence of real world, identifying matter
Frame the necessary conditions for membership of a class (in a logical ontology) in terms of what would be found (true) in data when the class of thing is there
Inference as distinct from meaning in the original sense
From the data you can infer that a thing exists in reality
Real meaning – by definition mostly does not rely on data!

6. Examples
Meaning of Bank: framed in terms of legal capabilities and rights
Data surrogate: banking license
Ownership and Control
Confer certain rights and involve certain capabilities
These are social constructs not data
Data surrogates: documents, deeds, shares etc.

7. Conceptual and Physical Ontologies
Business Conceptual
Ontology (CIM)
Business
The Language Interface
Extract and Optimise
Technology
Operational Ontology
(PSM)

8. Types and Datatypes Business Technology Business Conceptual
Ontology (CIM)
Business
The Language Interface
Extract and Optimise
Technology
Data types
Operational Ontology
(PSM)
Platform specific matter

9. Data as Real Things
There are also things in the real world that are made of information
Information Construct as a high level class of Thing
Names, publicly issued IDs, ratings, codes, documents, messages etc.
These are on the “real world” side of the Rhombus
There will also be data “about” these

10. Other implications Names: Dates and Times Value Types
Name as an ontological class of thing
Name as text
Dates and Times
Temporal matter
XSD datatypes for Date, DateTime etc.
What is “Date”?
Value Types

11. Data: The T Box Things the data is about about Data-focused Extensions
represents
Data-centric Intensions

12. Data Delta ẟD about Things the data is about represents
Data-focused Extensions ẟD
The
Real
Things

13. Data Delta: ẟ => 0 ẟD => 0 about Things the data is about
represents
The
Real
Things
ẟD => 0

14. Information Kinds Names Textual material Dates and Times
Yes or No (or maybe)
Numbers
Whole numbers
Numbers with decimal places
Positive Numbers
Fractions
Percentages
URL
Pictures
Sounds
Words
Letters
And many more…

15. Datatypes Text Dates and Times Boolean Numeric datatypes URL/URI
Restricted text
Unrestricted text
Dates and Times
Boolean
Numeric datatypes
Integer
Float
Positive integer, positive float
URL/URI
Other information kinds are rendered in files, for example vector graphics, rich text, video and sound formats

16. Relating information kinds to datatypes
Different kinds of information need to be stored in a computer
Datatypes determine how these are stored for optimum memory usage
XML datatypes differ on this, in that textual conventions are used to render different datatypes, which must then be translated to application datatypes for processing if needed
Numeric datatypes allow for arithmetic calculations on the data
Textual datatypes allow for alphanumeric sorting

17. Implications for Ontologies
OWL ontologies use a rather restricted sub-set of the XML datatypes set
These are chosen in line with operational constraints on reasoner applications
These constraints have no place in a conceptual ontology
We need to translate real world kinds of information into OWL XML datatypes for any onward processing in operational ontology applications
Just we also will if using the ontology to derive applications in other architectures
Also a conceptual ontology must be presented to the business for validation in their own terms
SMEs not only do not know about technical datatypes, they officially don’t care!
Let’s look at some example comparisons

18. Names A name identifies a person or a thing
A name typically consists of a set of sounds and a corresponding set of letters making up the spoken word which is that person’s name
Translating to datatypes
Names are generally rendered as textual datatypes, often with a length limitation to suit the application
Now in the real (conceptual) world a name may be infinitely long, whereas in computer systems we come up with some sensible length limit. A name may be in any alphabet, while in computer systems it must be rendered in some alphabet supported by Unicode or something similar. So for instance the Scottish name Menzies can't be accurately represented because the Scottish letter Yau never made it into these computer alphabets (someone correct me if this is somewhere in Unicode) and so it is generally rendered with a Z - and has been for so long, including in typewriters, lead type etc. that the use of Z has become the accepted rendition.

19. Scotland’s Missing Letter
The Scots alphabet has one more letter than the English alphabet
Yogh:
This never made it onto the typewriter and so is missing in the ASCII character set and many others
Notes from
The last letter, usually referred to as yogh, still appears in Scots personal and place names, though is usually written Z z. This has lead to the spelling-based pronunciations of names like Menzies [mɛnziz] - should be [mɪŋʌs], Dalziel [dɪjəɫ] and Monzie [mɔne]

20. Names: Some edge cases
The Artist Formally Known as the Artist Formerly Known as Prince
Name rendered as a symbol only
Music press shortcut (using ASCII):
"Prince changed his name to the unpronounceable glyph O(+> in 1993, which he used until During that time, he was more frequently referred to as "the artist formerly known as Prince," and his new symbol was not embraced by most fans."

21. Answers and Booleans Business (reality)
There are certain kinds of question for which the answer is
Yes or No
Yes, No, Maybe
Yes, No, Maybe, don’t know, it isn’t as simple as that…
Some propositions necessarily require a binary answer
“Is today Wednesday” only has two valid answers
The “don’t know” option is covered under the Open World Assumption
Others do not
Computer
A boolean returns the truth value of a proposition
Usually a good match for many of these question types
It does not mean strictly the same thing though
Many data sources use booleans where a binary choice of values exist in the data
Contract: isNegotiable (boolean)
It either is or it isn’t.

22. Dates and Times Business (reality) Computer OWL
A date is simply an index to a day
The first of September 2014
6 Elul, 5774
The calendar is the scheme which defines that index
Computer
Date
DateTime
OWL
DateTime only (for some egregious reason)
Therefore the data which is fed in has to have values set as midnight (making them indistinguishable from things that actually happen at dead of night)
The day I wrote this slide

23. Values

24. Core Definitions (JCGM 200:2008)
Quantity : property of a phenomenon, body, or substance, where the property has a magnitude that can be expressed as a number and a reference
Value of a quantity: value number and reference together expressing magnitude of a quantity
Also DOLCE/Guizzardi “Quale”
Measurement unit (unit of measurement unit) :real scalar quantity, defined and adopted by convention, with which any other quantity of the same kind can be compared to express
Kind of quantity (quantity kind): aspect common to mutually comparable quantities
Also DOLCE /Guizzardi “Quality Space”

25. Why Model Values, Quantities and Units?
Many models have “int” and “String” as the types of Characteristics of things
This does not represent their semantics, only how they are represented in the computer
Values have semantics beyond their representation as strings or numbers
E.g. a social security number has source, uniqueness and identity implications
E.g. An exchange rate on the London and N.Y. exchanges are not the same
Programming example: Assigning a “description (string)” to a “name (string)” is an error.
Examples of unit confusion are well known
E.g. First mars lander crashes due to unit confusion
When consuming, federating or translating data it is dangerous to be unsure what the data represents
Different representations of the same concepts may use or expect different units expressed in different ways
So to federate, integrate and translate – value kinds & units as well as the concepts they represent must be clear and consistent across independent models
But, specifying units in conceptual reference models is over-commitment and ignores “local needs and conventions”
Making values, quantities and units “first class” in a language encourages use and consistency.
Using the MDA (Model Driven Architecture) pattern allows implementation diversity
libraries of common value types and units allows for interoperability and reuse

26. Quantity Kinds & Units in SMIF Profile
For numeric characteristics, we want to know what it means (e.g. Temperature), not the kind of number (Real).
<<Quantity Kind>> is an aspect common to mutually comparable quantities represented by one or more units.
A “unit value” represents a quantity kind, there are multiple units representing temperature.
A physical representation would then represent the unit as some kind of number in a specified unit.
4/7/2019
Threat & Risk

27. Data represents concept
Mapping
Rule
Represents
STIX XSD
Concepts
Green line is “Represents”
Rules specify mapping details
11/18/2015
OMG Threat & Risk for STIDS 2015

28. Conceptual Extensions
Mid Level Ontologies
Domain independent concepts
Reusable Semantics from other domains
Aim to identify and re-use available academic work on conceptual abstractions where these exist
Subject to their fitting within the same set of theories as your conceptual ontology (or adapt as needed)
A considerable body of such work exists in the applied ontology field
© Hypercube 2015

29. Semantic Abstractions
Inevitable by-product of the “What kind of Thing is this?” question
Ontologies are built around a classification hierarchy (“Taxonomy”) of kinds of thing
This is key to meaningful ontologies
Enables disambiguation across business contexts
Not a technology activity
Examples: Contract, Credit, Asset etc.
© Hypercube 2015

30. Semantics Re-use Research and identify re-usable content semantics
In formal published ontologies
Business models in non ontological (non FOL) formats
Technical / messaging standards to “reverse engineer” into semantics
Pre-requisite: identify abstractions needed to support the specification concepts
Examples:
Transaction semantics
Legal / contractual etc.
Real Estate (for mortgage loans)
© Hypercube 2015

31. Upper Ontology Examples
Independent Relative and Mediating Things
Continuant and Occurrent things
Others

32. Approaches to Context Context as a Class Kinds of Context
Mediating Thing partition
Kinds of Context
Who What When Where hoW
Why?
Everything as Context

33. Upper ontologies Overview Relative Things (qua entities)
Partitions v Geometries
Relative Things (qua entities)
Time sensitive things
General UO choices
Oher partitions
Information construct
Dispositions, other philosophical stuff we probably don’t need

34. Partitioning
In general there seem to be 2 things to consider with top level ontologies:
How the world (the domain if discourse) is divided up: Partitions
How these concepts are framed: Treatments
4D v3D / 3D+
Endurantism v perdurantism
Mereology (parts and wholes)
Dimensions, values, quantities
etc.

35. Stance Consider the ontological stance of the ontology
Possible stances (not exhaustive)
Realist: the ontology only represents things that have some extension in time and space in some real or possible world
Idealist: Ontology must be able to represent concepts whether or not these have physical or temporal extent
For risk, business planning, commitments etc. concepts are essential
Risk event is avoided in any world in which it is a risk event
Plans, commitments, Prescriptive processes etc.
Realism may also include social constructs

36. Relative Things Added Pontoon
This is another thing you can land a boat on
Some jetties you can’t land or unload, they are just for managing tidal currents
What jetties are for versus what they are
What pontoons are for (always built fo that purpose)
What does it mean to be something defined by its function:

37. Landings – a Relative Thing

38. Upper Ontology Partitions

39. The ‘Relatives’ Partition
Everything which may be defined falls into one of three categories:
Thing
Independent Thing
Relative Thing
Mediating Thing
“Thing in Itself”
e.g. some Person
Thing in some context
e.g. that person as an employee, as a customer, as a pilot…
Context in which the relative things are defined
e.g. employment, sales, aviation

40. Definitions: Sowa
Independent categories are characterized by monadic predicates defined in terms of some entity x by itself (including its inherent parts and properties) and not in terms of anything external to x.
Relative categories are characterized by dyadic predicates that relate an entity x to some external entity y that can exist independently of x.
Mediating categories are characterized by triadic or higher predicates that show how an entity x mediates two or more entities (y,z, . . .) and thereby establishes new relationships among them.

41. Definitions: FIBO Conceptual Framework
Independent Thing: a thing in its own right
Relative Thing: A thing defined specifically and only in relation to some context.
Mediating Thing: A thing which brings together two or more independent things into some relation, usually resulting in their being defined as Relative Things.

42. The ‘Relatives’ Partition
Thing
Independent Thing
Person
Relative Thing
Employee
Customer
Pilot
Mediating Thing (context)
Employment
Sales
Aviation
“played by” relationship:
That which performs the
role of the “Relative Thing”

43. The ‘Relatives’ Partition
Thing
Independent Thing
Person
Relative Thing
Employee
Customer
Pilot
Mediating Thing (context)
Employment
Sales
Aviation
“In context” relationship:
Context in which the Independent Thing
performs the role of the “Relative Thing”

44. The ‘Relatives’ Partition
Thing
Independent Thing
Person
Relative Thing
Employee
Customer
Pilot
Mediating Thing (context)
Employment
Sales
Aviation
“played by”
“In context”
Everything which may be defined falls into one of these three categories
In order to complete a model of business terms and definitions, all three are needed
This extends beyond conventional ontology applications into a full and legally nuanced conceptual ontology

45. Subsequent Refinement
Thing
Independent Thing
Relative Thing
Mediating Thing (context)
“played by”
“In context ”
Actually
playedBy relation may sometimes be another Relative Thing
inContextOf is sometimes an Independent Thing
Promoted both to Thing
Comparable with BFO
Not all contexts are “Mediating Thing”

46. Mediating Thing as Context
Not all relative things are relative to a thing that we would consider as a mediating thing,
but contexts always create relative things
Mediating Thing is one kind of context in that it brings together 2 or more things in defined roles or functions
Includes business areas, process workflow, customer ad client relationships etc.

47. Hierarchy of Contexts: Pilot
Person
Pilot
Aviation
played by
In context
Commercial Pilot
In context
Commercial Aviation
Pilot of M101
In context
Flight MH101
John
Your pilot today
In context
Flight MH101 on 18 Oct 2017
played by

48. Hierarchy of Contexts: Customer
Legal Person
Bank Client
Bank Client Relationship
played by
In context
Retail Customer
Retail Customer Relationship
Natural Person
Organization
Current Account Holder
Current Account Relationship
Loan Borrower
Loan Relationship

49. Ontology Partitioning 2
Continuant and occurrent Things
Also known as Endurant and Perdurant
Ref: Guarino and Welty

50. Ontology Partitioning 2
Thing
Continuant
Occurrent

51. Ontology Partitioning 2
Thing
Continuant
Occurrent
Continuant: where it exists it exists in all its parts
Even if these change over time
Occurrent: the concept is only meaningful with reference to time

52. Ontology Partitioning 2
Thing
Continuant
Person
Contract
Pilot
Occurrent
Event
State
Etc.
Continuant: where it exists it exists in all its parts
Even if these change over time
Occurrent: the concept is only meaningful with reference to time

53. Ontology Partitioning 2
Thing
Continuant
Person
Contract
Pilot
Occurrent
Event
State
Etc.
Things which are independent or relative are also either continuant or occurrent

54. Ontology Partitioning 2 Example
Thing
Continuant Me
Occurrent
My life
Me: where I exist I exist in all my parts
Even if these change over time
My life: happens over a period of time and cannot be defined without time

55. Ontology Partitioning 3
Thing
Concrete
Abstract

56. Ontology Partitioning 3
Thing
Concrete
Abstract
Concrete: A physical thing
Or a virtual thing in some reality
Abstract: the concept is only meaningful as an abstraction from reality

57. Ontology Partitioning 3
Thing
Concrete
Pillar of Stone
Financial Instrument
Wheelbarrow
Abstract
Goal
Resolution
Desire
Not as simple as physical v non physical
Concrete: not limited to 3D physical reality
Abstract: no physical or virtual expression

58. Some Popular Upper Ontologies

59. Sowa KR Lattice

60. Sowa Partitions (FIBO* and Semantic Shed)
* Developed as part of conceptual framework for FIBO but not published as part of the FIBO standard

61. DOLCE

62. TUpperWare

63. A View on GFO
Ontology Summit 2018, Feb 07

64. YAMATO

65. IDEAS

66. GIST

67. BFO

68. OBO ~2005 initial OBO Foundry suite TO TIME GRANULARITY CONTINUANT
RELATION
TO TIME
GRANULARITY
CONTINUANT
OCCURRENT
INDEPENDENT
DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical Entity
(FMA, CARO)
Organ
Function
(FMP, CPRO)
Phenotypic Quality (PaTO)
Biological Process
(GO)
CELL AND CELLULAR COMPONENT
Cell
(CL)
Cellular Component
(FMA, GO)
Cellular Function
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
Molecular Process
~2005 initial OBO Foundry suite

69. Questions?
Next: Deep dive into some upper ontology partitions