How to Create a Golden Ontology - Hypercube

Conceptual Semantics: How to Build a Golden Ontology Webinar 8 April 2015 Mike Bennett Hypercube Ltd. 1 Jabberwocky Twas brillig and the slithy toves Did gyre and gimble in the wabe All mimsy were the borogoves

And the mome raths outgrabe - Lewis Caroll 2 Agenda 3 things you need to know about ontologies Words are not Concepts Syntax is not Semantics Meaning is not Truth 3 things you need to do to build a Golden reference ontology Classification

Abstraction Partitioning 3 ways to use a Golden Ontology Querying across legacy data sources Mapping and data integration Reasoning with Semantic Web applications 3 The Knowledge-enabled Enterprise Reporting etc.

Enterprise Conceptual Ontology Legacy Data Sources and Systems Model Positioning Conceptual Model Realise Logical Model (PIM) Implement Physical Model (PSM)

Copyright 2010 EDM Council Inc. 5 Model Positioning Conceptual Model Business The Language Interface Realise Logical Model (PIM)

Technology Implement Physical Model (PSM) Copyright 2010 EDM Council Inc. 6 3 Things you need to know Words are not Concepts

Syntax is not Semantics Meaning is not Truth 7 1. Words are not Concepts 8 The Meaning of Football Football (USA)

Football (everywhere else) These are heteronyms (same word, different meaning) 9 The Meaning of Football Soccer ball (USA) Football (everywhere

else) These are synonyms (different word, same meaning) 10 The Meaning of Football Football (a ball) Football (the game)

These are heteronyms (same word, different meaning) 11 The Meaning of Loan Loan: An amount drawn down by a borrower on a given date, from a lender, with terms for repayment and interest payment Not everything suffixed Loan fits into the set of things logically defined with this definition Construction Loan: a credit facility, with periodic draw-downs (loans) against agreed construction milestones

Student Loan: may be a credit facility or a loan depending on how the agreement is structured 12 The Meaning of Fund Fund Pool of Resources Financial Instrument is a

is a Negotiable Financial Instrument Legal Entity is a Fund Entity is a is held by a

Fund is traded as a Fund Unit is a is managed by a Fund Management Company The term (label) Such and Such Fund may be used in normal speech to refer to any one of the Fund Entity, the Fund (as a pool of resources) and the fund unit

13 The Meaning of Legal Entity Legal Entity (1) an autonomous entity which is capable of legal liability Synonym: Legal Person Legal Entity (2) a partnership, corporation, or other organization having the capacity to negotiate contracts, assume financial obligations, and pay off debts, organized under the laws of some jurisdiction Synonym: LEI Legal Entity Source: ISO 17442

14 2. Syntax is not Semantics 15 Lets look at Jabberwocky again Twas brillig and the slithy toves Did gyre and gimble in the wabe All mimsy were the borogoves And the mome raths outgrabe

16 Lets look at Jabberwocky again Twas brillig and the slithy toves Did gyre and gimble in the wabe All mimsy were the borogoves And the mome raths outgrabe What are raths? When is brillig? What kind of thing is a tove? Whats a wabe?

and the wabe of what? How does one gyre? How does on gimble? What is a borogrove? What makes them mimsy? What does it mean to be mome? What does outgrobing consist of exactly? 17

Lets look at Jabberwocky again Twas brillig and the slithy toves Did gyre and gimble in the wabe All mimsy were the borogoves And the mome raths outgrabe What are raths? When is brillig? What kind of thing is a tove? Whats a wabe? and the wabe of what?

How does one gyre? How does on gimble? What is a borogrove? What makes them mimsy? What does it mean to be mome? What does outgrobing consist of exactly? Tis all nonsense Or is it? 18

Syntax and Semantics A little syntax goes a long way The English syntax in Jabberwocky narrows down the space of possible things in the world that the poem can be talking about We can identify some of the semantics of the concepts from the syntax that links them in formal syntactical relationships But not all! The same would be the case with a logical syntax But it doesnt deal with all the semantics! 19

Logical Syntax Try this test: Take a logical data model expressed in e.g. UML Transform the UML into OWL What do you have? 20 Logical Syntax Try this test: Take a logical data model expressed in e.g. UML Transform the UML into OWL What do you have?

You have a logical data model in OWL! Changing the syntax did not make it an ontology Similarly but less obviously, a set of code lists in OWL is not an ontology! 21 3. Meaning is not Truth 22 Logical Syntax Allows us to determine the truth value of propositions

23 Logical Syntax Allows us to determine the truth value of propositions A If (A and B) then C C B 24

Logical Syntax Allows us to determine the truth value of propositions A If (A and B) then C C B Given a truth value for A and for B, then C is true 25

Web Ontology Language (OWL) Declarative statements about kinds of thing and properties of those kinds of thing Framed in a sub set of First Order Logic (FOL) Lets us make logical statements about the relationships between kinds of thing OWL is limited in its expressive power, but what we can express depends on how we frame the semantics of the concepts (the kinds of thing and the relationships among them) The syntax allows us to say things clearly and unambiguously in a way that is readable by machines and by people It is computationally independent! 26

Meaning is not Truth The logic lets us infer truth values based on assertions in the model and in the available data Running a reasoned will uncover these relations Logic (truth values) provides a means to an end This is not the same as saying logic / truth is semantics Some people would say this model has no semantics when they mean it has no logic from which to determine truth values 27

Semiotics (after C. S. Peirce) 3 Things you need to do Classification Abstraction Partitioning 29 Capturing Meaningful Concepts For each kind of Thing in the ontology (each class):

What kind of thing is this? What distinguishes it from other things? 30 What is an Ontology? An ontology is a representation of real things using formal logic 31 Defining a Kind of Thing We start with some kind of thing

Some kind of thing Defining a Kind of Thing We ask just two questions about this kind of thing: What kind of thing is it? What distinguishes it from other things? Some kind of thing What kind of thing is it? Animal

Vertebrate Bird Waterfowl Some kind of thing Mammal Invertebrate

Fish What distinguishes it from other things? Animal Vertebrate Bird Invertebrate

Mammal Fish Waterfowl Walks like a duck Some kind of thing Swims like a duck Quacks like a duck Its a Duck!

Animal Vertebrate Bird Invertebrate Mammal Fish Waterfowl

Walks like a duck Swims like a duck Quacks like a duck FIBO Example: Business Entities Copyright 2010 EDM Council Inc. 37 FIBO Example: Credit Default Swap 38

1. Classification Taxonomic relations Taxonomies in general may be based on several kinds of hierarchical relations We use only the is a relation (sub class of) Faceted Classification Allow multiple inheritance of classes Derivatives -> contract types (forward, option, swap) Derivatives -> underlying types (commodities, rates, indices, instruments) 39

Faceted Classification 40 Hierarchical Taxonomies Typically use one-to-many relationships Some one-to-many relationships are not associated with hierarchies The relationship between a person and his/her phone numbers Some hierarchical relationships are not one-to-many

A thing with only one part (e.g., Wyoming has only one congressional district) Some hierarchies have no relationships at all A hierarchy of income brackets Transitive recurrent relationships Common varieties: Type of, part of Military hierarchies: Commands Relationships that apply particularly to finance? In addition to type of and part of

Transitivity violations A certain rock is considered to be a chair. Chairs are considered furniture. But the rock is not considered furniture An executive supervises a middle manager. The manager supervises a technician. But (perhaps) the executive has no relationship to the technician. Usually involve second-order (extrinsic) concepts Polyhierarchies Categories with multiple superordinates Dog can be nested under both Canine and Pet Alternative treatments exist for pet

Financial Examples An IR Swap is both a swap and an interest rate derivative Swaption can be nested under both Swap and Option Polyhierarchies may be Expressed directly through multiple inheritance Ordered (determine sequence in which to apply a given facet) Classification: To infer or not to infer? 44

Pizza: Asserted Pizza: Inferred To infer or not to infer? Can use logical restrictions to assert things about something which would place it in a given category HOWEVER This is then unreadable to the business SMEs Make the faceted taxonomy explicit so SMEs can review it And then remove the additional relations in operational ontology

Replacing these with restrictions as above OR Include restrictions, run the reasoner and show SMEs the results in a business-facing format 47 2. Abstraction How to abstract concepts Top down versus bottom up Where to stop? Use of use cases

Not everyone is comfortable with abstractions This is where you really have to think about meaning Also where you need to facilitate SME review input carefully 48 Abstract Thinking What kind of Thing is An address? An address is an index to a location A client? A customer? Related to a product / service or to a whole business?

A securities exchange? How does it differ from a street market? What does an exchange have in common with a street market? Where does the classification hierarchy (taxonomy) divide? 49 Abstract Thinking What kind of Thing is An address? An address is an index to a location A client? A customer? Related to a product / service or to a whole business?

A securities exchange? How does it differ from a street market? What does an exchange have in common with a street market? Where does the classification hierarchy (taxonomy) divide? 50 Abstracting concepts Lets look at the use case question 51 Use Case Use Cases

Application use case: What the user expects the application to do This is behavioural (what it does) not structural (data / ontology) Competency questions etc. Applies to ontology based applications as much as any other kind of application Conceptual Model Use Case

This is NOT an application its a computationally independent model 52 This is not a more abstract model of the solution Conceptual Ontology Business The Language Interface Realise

Logical Data Model (PIM) Technology Implement Physical Data Model (PSM) 53 This is not a more abstract model of the solutionIts a concrete model of the problem!

Conceptual Ontology Business The Language Interface Realise Logical Data Model (PIM) Technology Implement

Physical Data Model (PSM) 54 Use Cases in Conceptual Ontology The conceptual model needs to support all of the applications and data sources for which it is intended to provide the computationally independent (business) view Use case informs: The SCOPE of the model what are the data elements for which the enterprise needs formally defined concepts? The Ontological Commitments: Granularity of concepts Theory of the World

Model theories / partitions From this we can get an idea of how far to abstract concepts in the ontology 55 Pizze Suppose we have a nice pizza ontology It covers concepts like pizza base, pizza topping How far should we abstract from this? Pizza Ontology Just pizze

Pizza Base Pizza Topping 56 Pizze Suppose we have a nice pizza ontology It covers concepts like pizza base, pizza topping How far should we abstract from this? Baked Goods Ontology Baked Food Bread

Pizza Ontology Pizza Base Pizza Topping 57 Pizze Suppose we have a nice pizza ontology It covers concepts like pizza base, pizza topping How far should we abstract from this? Food Ontology All food

Food Baked Goods Ontology Bread Pizza Ontology Pizza Base Pizza Topping 58 Pizze Suppose we have a nice pizza ontology

It covers concepts like pizza base, pizza topping How far should we abstract from this? Food and Drink Ontology All food and drink (Pizza, wine etc.) Digestible Thing Food Ontology Food Baked Goods Ontology

Answer: it depends on the scoping requirement for the ontology Bread Pizza Ontology Pizza Base Pizza Topping 59 A Taxonomy bird

robin canary Some Observations on Abstraction Working with subject matter experts requires careful management of the knowledge acquisition process Pitfalls: Silo-based assumptions Localized jargon Reliance on words Make sure SMEs fully understand the set theoretic nature of the presentation

materials Make sure they understand synonyms, heteronyms Make sure they are aware of any ontological abstractions or buckets you may have in the ontology (these will not correspond to anything in the SMEs own experience!) 61 Some Observations on Abstraction Slithy Toves

Why do the SMEs mention that they are slithy at all? In the wild, most toves are glumpfy, however these are not relevant to this line of business Do they have to note they are slithy for reporting purposes? In parts of California, toves are neither slithy nor glumpfy Someone on the team has a partner who works in a zoo and has never come across this concept Think beyond the context of the SMEs! Identify what the SMEs context is (write it down!) What other concepts are within the scope / conceptual use case? Will your ontology need to interact with other ontologies beyond the immediate use case (e.g. schema.org or global standards?); if so, allow for all the realistic properties a tove may have both in captivity and in the wild!

62 3. Partitioning 63 Partition I: Independents and Everything which may be defined falls into one of Relatives three categories: Thing in Itself

Thing in some context e.g. some Person 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 64 Independents and Relatives Has identity relationship: That which performs the role of the Relative Thing

65 Independents and Relatives In context of relationship: Context in which the Independent Thing performs the role of the Relative Thing 66 Independents and Relatives

Has identity In context of 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 67 Why does this Matter? Define all concepts of interest to the business Map to data which is framed in a context specific way Assist with restructuring data for re-use across the firm

E.g. business entity versus client / counterparty and other role-specific 68 Partitioning II: Continuants and Occurrents Continuant and occurrent Things Ref: John F Sowa Also known as Endurant and Perdurant Ref: Guarino and Welty Continuants and Occurrents

Continuants and Occurrents 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

Continuants and Occurrents 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 Ontology Partitioning

Things which are independent or relative are also either continuant or occurrent Continuants and Occurrents Example Me: where I exist I exist My life: happens over a in all my parts period of time and Even if these change cannot be defined over time

without time Why does this Matter? Frame concepts which have a temporal component which are of interest to the business Events, activities States Statuses, prices, other time-variant concepts Provide a basis for ontological modelling of business process This brings the two sides of development (structural and behavioural) into the same conceptual model

75 Partitions III: Concrete and Abstract Concrete and Abstract Concrete: A physical thing Or a virtual thing in some reality Abstract: the concept is only meaningful as an

abstraction from reality Concrete and Abstract Not as simple as physical v non physical Concrete: not limited to Abstract: no physical 3D physical reality or virtual expression Why does this Matter? Distinguish between concepts which have a direct physical (or electronic) expression from those which dont Talk about goals, strategies etc.

Portfolio strategies needed for compliance etc. Business goals form part of formal model of risk concepts Business motivation models can be brought into the same conceptual framework Distinguish abstract metrics from concrete amounts of stuff 79 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 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. 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) 82 Semantic Grounding for Businesses What are the basic experiences or constructs relevant to business?

Monetary: profit / loss, assets / liabilities, equity Law and Jurisdiction Government, regulatory environment Contracts, agreements, commitments Products and Services Other e.g. geopolitical, logistics 83 3 ways to use conceptual ontology

Querying across legacy data sources Mapping and data integration Reasoning with Semantic Web applications 84 1. Querying across Legacy Data Sources Recommended Architectures Wrappers and Adapters When to stand up a triple store 85

Knowledge-enabled Enterprise Enterprise-wide Concept Model Ontology to Legacy Database Adapters Legacy Data Sources and Systems Knowledge-enabled Enterprise Risk, Compliance etc. Semantic Queries Enterprise-wide Concept Model

Ontology to Legacy Database Adapters Legacy Data Sources and Systems Knowledge-enabled Enterprise Risk, Compliance etc. Reporting Semantic Queries Enterprise-wide Concept Model Ontology to Legacy Database Adapters

Legacy Data Sources and Systems Using Wrappers 89 Converting Relational Data to Graphs 25 Alice Person

ID NAME AGE CID 1

Alice 25 100 2 Bob NULL 100

Bob City CID

NAME 100 Austin 200 Madrid

www.capsenta.com Austin Madrid Integration with Ultrawrap Target Ontology

Source 1 Ontology Source 2 Ontology Source N Ontology Ultrawrap Ultrawrap

Ultrawrap Source DB 1 Source DB 2 Source DB N www.capsenta.com Target Ontology

Federator Federator Source 1 Ontology Source 2 Ontology Ultrawrap Ultrawrap

Ultrawrap Source DB 1 Source DB 2 Source DB N www.capsenta.com Source N

Ontology Solution Architecture Hybrid Model Reporting Query Response Conceptual Ontology Source DB 2

Source N Ontology Target Ontology Graph Triplestore Source DB N TL

Source P Ontology www.capsenta.com Source DB P E E Source DB 1 Source 2

Ontology TL Source 1 Ontology Source Q Ontology Source DB Q 93 2. Mapping and Data Integration The Simple Knowledge Organization System (SKOS)

Extending SKOS Mapping with SKOS Mapping Challenges 94 Extending SKOS SKOS Provides the following constructs for semantic relations between concepts: broader (hierarchical relation) narrower (hierarchical relation) related (associative relation) In the SKOS Primer the use of broader and narrower is explicitly given as including

both type relations and whole-part relations. Element semantics broader means has broader concept NOT is broader than At this level, transitivity or the lack of transitivity is not stated 95 Narrower Semantic Relations In the SKOS Primer: Type of Relation

Suggested element name Generic broaderGeneric Part-whole broaderPartitive Instance-class broaderInstantive

Loehrlein et al (Open Financial Data Group) Type of Relation Topic hierarchy Authority / power hierarchy Located in hierarchy Generic (type hierarchy) Inclusion sets 96 Topic Relations A book about French grammar is a kind of book about French they are in a type

hierarchy, as books. BUT French grammar is NOT a kind of French. These are in a topic hierarchy not a type hierarchy. SKOS supports both type and topic hierarchies. We need to refine the distinction between these. Books about French are kinds of book about language, which are kinds of books which are kinds of works. There is a consistent kind of relationship between a work, and the subject of that work. Models are also kinds of works.

Elements in a logical data model design are disposed in a type hierarchy Each have a relationship to the topic of that data element. 97 Topic Relations 98 Topic Relations: Mapping 99 Topic Relations: Financial

100 Suggested Extensions Insert broaderMatch to map across concept schemes Insert narrowerMatch to map across concept schemes 101

Mapping: Ontology to Data Model 102 Mapping: Data Model to Ontology 103 3. Reasoning with Semantic Web Applications Logical Ontologies Design Guidelines Stand-alone ontology design techniques and practice What works with an enterprise conceptual ontology and what

doesnt? Striking the balance! 104 Application vs. Reference Ontology Application Ontology Built to support a particular application (use case) Reused rather than define terms Skeleton structure to support application

Terms defined refine or create new concepts directly or through new classes based on inference Reference Ontology Intended as an authoritative source True to the limits of what is known Used by others Internal Consistency Semantics

Graph has logical relations between elements These correspond to the relations between things in reality Automated reasoning checks the deductive closure of the graph for consistency and completeness Internal Consistency Semantics Graph has logical relations between elements These correspond to the relations between things in reality Automated reasoning checks the deductive closure of the graph for consistency and completeness

Internal Consistency Semantics The more detailed logic there is in the application ontology, the more confident we can be that it reflects only one set of things and their relation in reality Like Jabberwocky Or a crossword solution This allows for stand-alone ontologies to do very powerful processing of knowledge in an application This is not incompatible with the techniques described for conceptual ontology modelling IFF it is done right! However, some techniques which are appropriate for stand-alone operational ontologies would not be compatible with a conceptual enterprise ontology Decide whether to have application and conceptual ontologies in separate namespaces, or

satisfy both sets of requirements in one namespace 108 Example: Trajectory Ontology Illustrated using Visual Ontology Modeler from Thematix 109 Property Domains and Ranges Application (operational) ontology: Make the domain and / or range as general as possible (e.g. Thing) so it can

be reused later Corresponds to a very vocabulary centric approach to ontology development (reuse common words with less dependence on their meaning) Enterprise conceptual ontology: Domain: the most general class of thing which could possibly have this property Range: the most general class of thing in terms of which this property may be framed 110 Conceptual versus Operational

Ontology Technique Conceptual Operational Deep subsumption hierarchy (taxonomy) YES Not advised

Properties with no domain and range NO YES Re-use of underspecified properties NO With caution Enough to

disambiguate As much as possible Minimal YES Property chains YES If possible

Property characteristics YES Subject to operational constraints Restrictions on classes Cascades of restrictions (restrictions on restrictions / unions) 111

Summary Conceptual ontologies: knowledge representation principles Use the KR and Applied Ontology literature! Think of meaning Ground concepts in semantic primitives Syntax is not semantics 112 Thank You! One Day Conference London 20 May

GBP 95 if booked before 17 April (then 145) USA contact Mike for details / express interest Modular on line training 9 sessions based on the structure of this webinar Chat Log from todays call will be answered and the answers circulated to attendees www.hypercube.co.uk 113

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