Hypercube Ltd. Conceptual Ontology Engineering Tutorial Session 3: The Data Dimension; Top Level Ontologies Mike Bennett FOIS JOWO Cape Town, South Africa September 2018 1 Session 3 The Data Dimension Introducing Top Level ontology (Upper Ontology)
Some popular TLOs 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
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 A member of one or more sets of of all towers (E.g., the actual one record structures containing tower we call the Eiffel Tower) observations (E.g., one that represents the actual Eiffel Tower) Represents #tower123 Jim Logan, NoMagic 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! 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.
6 Conceptual and Physical Ontologies Business Conceptual Ontology (CIM) Business The Language Interface Extract and Optimise Technology Operational Ontology (PSM) 7
Types and Datatypes Data types Business Conceptual Ontology (CIM) Business The Language Interface Extract and Optimise Technology Data types
Operational Ontology (PSM) Platform specific matter 8 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 9 Other implications Names: 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 10
Data: The T Box aboutThings the data is about Data-centric Intensions Datarepresents focused Extensions 11 Data Delta about represents
DD Things the data is about Data-focused Extensions The Real Things 12 Data Delta: => 0 => 0 about
Things the data is about represents DD => 0 The Real Things 13 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 Datatypes Text 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 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 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 dont care! Lets look at some example comparisons 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 persons name Translating to datatypes Names are generally rendered as textual datatypes,
often with a length limitation to suit the application Scotlands 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 http://www.omniglot.com/writing/scots.htm 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 [mnziz] - should be [ms], Dalziel [dj] and Monzie [mne] 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 2000. 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." 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, dont know, it isnt as simple as that
Some propositions necessarily require a binary answer Is today Wednesday only has two valid answers The dont 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 isnt. Dates and Times Business (reality)
A date is simply an index to a day The first of September 2014 6 Elul, 5774 The day I wrote this slide 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) Values 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 Why Model Values, Quantities and Units? Many models have int and String as the types of Characteristics of things 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
This does not represent their semantics, only how they are represented in the computer 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 Quantity Kinds & Units in SMIF Profile 02/02/2020 For numeric characteristics, we want to know what it means (e.g. Temperature), not the kind
of number (Real). <> 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. Threat & Risk 26 Data represents concept
Mapping Rule Represents STIX XSD Green line is Represents Concepts Rules specify mapping details 11/18/2015 OMG Threat & Risk for STIDS 2015 27
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 28
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 29 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 30 Upper Ontology Examples
Independent Relative and Mediating Things Continuant and Occurrent things Others Approaches to Context 1. Context as a Class Mediating Thing partition 2. Kinds of Context Who What When Where hoW Why? 3. Everything as Context Upper ontologies Overview
Partitions v Geometries Relative Things (qua entities) Time sensitive things General UO choices Oher partitions Information construct Dispositions, other philosophical stuff we probably dont need 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.
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
Relative Things Added Pontoon This is another thing you can land a boat on Some jetties you cant 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: Landings a Relative Thing Upper Ontology Partitions 38
The Relatives Partition Everything which may be defined falls into one of three categories: Thing Independent Thing Relative Thing Thing in Itself Thing in some context
e.g. some Person e.g. that person as an employee, as a customer, as a pilot Mediating Thing Context in which the relative things are defined
e.g. employment, sales, aviation 39 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. 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. The Relatives Partition Thing Independent
Thing Person Mediating Thing (context) Relative Thing Employee Customer Pilot Employment
Sales Aviation played by relationship: That which performs the role of the Relative Thing 42 The Relatives Partition Thing Independent Thing
Person Mediating Thing (context) Relative Thing Employee Customer Pilot Employment
Sales Aviation In context relationship: Context in which the Independent Thing performs the role of the Relative Thing 43 The Relatives Partition Thing Independent Thing
Person Mediating Thing (context) Relative Thing Employee played by Customer Pilot Employment
Sales Aviation 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 44 Subsequent Refinement Thing In context
played by Independent Thing Relative Thing Mediating Thing (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 45 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.
Hierarchy of Contexts: Pilot Person played by Pilot Commercial Pilot Pilot of M101 John played by
Your pilot today In context In context In context In context Aviation Commercial Aviation
Flight MH101 Flight MH101 on 18 Oct 2017 Hierarchy of Contexts: Customer Legal Person played by Natural Person Bank Client Retail Customer
In context Bank Client Relationship Retail Customer Relationship Organization Current Account Holder Loan Borrower Current Account Relationship
Loan Relationship Ontology Partitioning 2 Continuant and occurrent Things Also known as Endurant and Perdurant Ref: Guarino and Welty Ontology Partitioning 2 Thing Continuan t Occurrent Ontology Partitioning 2
Thing Continuan t Occurrent Continuant: where it exists Occurrent: the concept it exists in all its parts is only meaningful with Even if these change over time reference to time Ontology Partitioning 2 Thing
Continuant Person Contract Occurrent Pilot Event State Etc.
Continuant: where it exists Occurrent: the concept it exists in all its parts is only meaningful with Even if these change over time reference to time Ontology Partitioning 2 Thing Continuant Person Contract
Occurrent Pilot Event State Etc. Things which are independent or relative are also either continuant or occurrent Ontology Partitioning 2 Example Thing
Continuant Me Me: where I exist I exist in all my parts Even if these change over time Occurrent My life My life: happens over a period of time and
cannot be defined without time Ontology Partitioning 3 Thing Concrete Abstract Ontology Partitioning 3 Thing Concrete Abstract Concrete: A physical Abstract: the concept is
only meaningful as an thing Or a virtual thing in some reality abstraction from reality Ontology Partitioning 3 Thing Concrete Pillar of Stone Financial Instrument
Abstract Wheelbarrow Goal Resolution Desire Not as simple as physical v non physical Concrete: not limited to 3D physical reality Abstract: no physical or virtual expression
Some Popular Upper Ontologies 58 Sowa KR Lattice Sowa Partitions (FIBO * and Semantic Shed) * Developed as part of conceptual framework for FIBO but not published as part of the FIB 60
DOLCE TUpperWare A View on GFO Ontology Summit 2018, Feb 07 63 YAMATO IDEAS GIST
BFO 67 OBO CONTINUANT OCCURRENT RELATION TO TIME INDEPENDENT DEPENDENT
GRANULARITY ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomica l Entity (FMA, CARO) Organ Function
(FMP, CPRO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Compone nt (FMA, GO) Cellular
Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Phenotypic Quality (PaTO) Molecular Function (GO)
Biological Process (GO) Molecular Process (GO) ~2005 initial OBO Foundry suite 68 Questions? Next: Deep dive into some upper ontology partitions
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