Hypercube Ltd Conceptual Ontology Engineering Tutorial Session 5

Hypercube Ltd. Conceptual Ontology Engineering Tutorial Session 5: Putting it to Work Mike Bennett FOIS – JOWO Cape Town, South Africa September 2018 1

Session 5: Putting it to Work • An example conceptual ontology: the Financial Industry Business Ontology (FIBO) – from the Enterprise Data Management Council – https: //spec. edmcouncil. org/fibo • • Extending and deploying conceptual ontology Example usage scenarios Deterring the appropriate ontology styles Corporate transformation with ontology

Introducing FIBO • FIBO is a collaborative effort among industry practitioners, semantic technology experts and information scientists to standardize the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions. FIBO is being released as a series of standards under the technical governance of the Object Management Group (OMG): https: //www. omg. org/intro/Finance. pdf • FIBO is based on the legal structures and obligations contained within the myriad of contracts that form the foundation of the financial industry. We have constructed this content into both a business conceptual and fully operational ontology that formally models the reality of how the financial industry operates. • The EDM Council is the author and steward of the Financial Industry Business Ontology (FIBO). 3 Copyright © 2010 EDM Council Inc.

Financial Industry Business Ontology (FIBO) FIBO is built on state-of-the-art collaboration technology and supported by documented and tested governance FIBO is a business conceptual model that precisely describes financial instruments, pricing, legal entities and financial processes (what they are and how they work) FIBO facilitates data harmonization across disparate repositories based on legal meaning and contractual obligation FIBO provides structural validation to ensure completeness, consistency and allowable values FIBO is expressed in the W 3 C standard (RDF/OWL) for flexible and scenariobased/inference analysis FIBO feeds analytical processes with trusted data and powers smart contracts

Debt Securities Sample Hierarchy: Bonds Page 5

Corporate Bonds Overview Page 6

Foundational concepts for Contracts Examples: Bonds Terms common to all debt Interest, Principal terms common to Bonds Terms specific to Municipal Bonds

Review Formats – 1. Basic SME View • Boxes and lines – draw a line between kinds of thing – – • • This is a characteristic or “property” of that kind of thing Defines a feature of something in terms of its relation to something else There also properties defined in terms of simple assertions (yes/no, text, numbers) Also show what kind of thing something is (classification hierarchy) Term bond has principal repayment terms bond principal repayment terms set Definition Synonyms A debt security, in which the authorized issuer owes the holders a debt and, depending on the terms of the bond, is obliged to pay interest (the coupon) and/or to repay the principal at a later date, termed maturity. It is a formal contract to repay borrowed money with interest at fixed intervals. Repayment terms for the principal on a bond has redemption terms Terms for the repayment of the Principal on a Bond security. Necessary Conditions? must be some Page 8

Reviews Formats 2. Additional Semantics Representation • Ask what it takes for something to be a member of a particular category of thing – • E. g. “Is it necessarily the case that…” This is added to the ontology by the semantics gurus – – This uses a notation called SIMF Logic and multiplicities corresponding to OWL semantics “restrictions” Page 9

FIBO: Scope and Content Upper Ontology FIBO Foundations: High level abstractions FIBO Business Entities FIBO Financial Business and Commerce FIBO Indices and Indicators FIBO Contract Ontologies Securities (Common, Equities) Securities (Debt) Derivatives Loans, Mortgage Loans Funds Rights and Warrants FIBO Pricing and Analytics (time-sensitive concepts) Pricing, Yields, Analytics per instrument class FIBO Process Corporate Actions, Securities Issuance and Securitization Future FIBO: Portfolios, Positions etc. Concepts relating to individual institutions, reporting requirements etc.

Using and Extending FIBO EXTEND DEPLOY Firm’s Business Conceptual Ontology There are two dimensions to the use of the FIBO ontology standard: 1. Deploying it in the IT environment 2. Extending with the firm’s own concepts App App However….

Using and Extending FIBO EXTEND Firm’s Business Conceptual Ontology The conceptual ontology is the firm’s own resource and should be extended with firm-specific concepts before being used in deployment. DEPLOY However…. App App

Deploying Extended BCO Customer BCO Common Logical Data Model Local LDMs DEPLOY Adapters Operational Ontologies

Deploying Extended BCO External Foundations semantics Conceptual Ontology Customer BCO Common Logical Data Model Local LDMs DEPLOY Adapters Operational Ontologies Logical data model from Logical Ontology Internal Correspondence semantics Logical Ontology

Mapping and Data Integration • Not as simple as that… • One concept may be modelled in different data element combinations • One data element may refer to a whole path through the ontology graph • Conceptual model must be general enough that a range of different logical designs can be referenced to it – E. g. stand up wide range of Relative, Occurrent etc along with property chains and intermediate classes © Hypercube 2015 How to Buiild a Canonical Reference Ontology 2015 -05 -04 15

Ontology to Data Model 16

Data Model to Ontology 17

Querying across Legacy Data Sources • Recommended Architectures • Wrappers and Adapters • When to stand up a triple store © Hypercube 2015 How to Buiild a Canonical Reference Ontology 2015 -05 -04 18

Challenges • Triple store and Data Management – Timeliness (how often to update) – Provenance – System of record – Data lineage • What if we could use the ontology to report on the data in situ?

Virtual Ontology Risk, Compliance etc. Reporting Semantic Queries Reference ontology R 2 RML based Ontology to Legacy Database Adapters Legacy Data Sources and Systems

Virtual Ontology Risk, Compliance etc. Logical Ontology Foundational semantics Reporting Semantic Queries Reference ontology R 2 RML based Ontology to Legacy Database Adapters Legacy Data Sources and Systems

Reasoning with Semantic Web • Logical Ontologies – Design Guidelines • Stand-alone ontology design techniques and practice • What works with an enterprise conceptual ontology and what doesn’t? • Striking the balance! © Hypercube 2015 How to Buiild a Canonical Reference Ontology 2015 -05 -04 22

Semantic Web Applications • Semantic Operational Processing Reasons over Data to Infer Classifications and Relationships Fixed Float IR Swap (Ontology) Machine Facing Definition is. Trading. With Business Entity Swap_Contract and has. Leg Fixed. Rate. Leg and has. Leg Floating. Rate. Leg Acme Inc Fixed Float IR Swap identifies type Inferred LEI 5001 Inferred type rty pa LEI Floating. Rate. Leg Trader LLC type Inferred Human Facing Definition An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments. Business Entity Interest Rate Swap h Leg 1 notional Leg 1 is inferred to be a Floating. Rate. Leg because any leg tied to an index is semantically defined as floating Semantic reasoning 1 David Newman, Wells Fargo Swap 100 h g 1 eg as. L e L Swap as index 1000000 LIB 0 OR currency USD LEI 7777 Leg 2 fixed. Rate 3. 5 % Data for an undefined Swap Contract before semantic reasoning performs classification and identification rty pa LEI is. Trading. With is a new property relationship that is inferred based on a semantic rule and can be queried Semantic reasoning 4 Swap is inferred to be a Fixed-Float IR Swap because one leg was inferred to be fixed and one leg was inferred to be floating fulfilling the definitions in the ontology 3 Semantic reasoning Inferred type Fixed. Rate. Leg notional 1000000 0 currency USD Leg 2 is inferred to be a Fixed. Rate. Leg because any leg tied to an interest rate is semantically defined as fixed 2 Semantic reasoning 23

Semantic Web Applications Physical Ontology Internal Consistency semantics (reasoning) • • Fixed Float IR Swap (Ontology) Machine Facing Definition is. Trading. With Business Entity Swap_Contract and has. Leg Fixed. Rate. Leg and has. Leg Floating. Rate. Leg Acme Inc Fixed Float IR Swap identifies type Inferred LEI 5001 Inferred type rty pa LEI Floating. Rate. Leg Trader LLC type Inferred Human Facing Definition An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments. Business Entity Interest Rate Swap h Leg 1 notional Leg 1 is inferred to be a Floating. Rate. Leg because any leg tied to an index is semantically defined as floating Semantic reasoning 1 David Newman, Wells Fargo Swap 100 h g 1 eg as. L e L Swap as index 1000000 LIB 0 OR currency USD LEI 7777 Leg 2 fixed. Rate 3. 5 % Data for an undefined Swap Contract before semantic reasoning performs classification and identification rty pa LEI is. Trading. With is a new property relationship that is inferred based on a semantic rule and can be queried Semantic reasoning 4 Swap is inferred to be a Fixed-Float IR Swap because one leg was inferred to be fixed and one leg was inferred to be floating fulfilling the definitions in the ontology 3 Semantic reasoning Inferred type Fixed. Rate. Leg notional 1000000 0 currency USD Leg 2 is inferred to be a Fixed. Rate. Leg because any leg tied to an interest rate is semantically defined as fixed 2 Semantic reasoning 24

Regulatory Reporting • Use granular semantics for reporting to regulators, central banks • Flexible access to data via separate semantic “layer” • Semantic querying – Fast turn-around of new reporting requirements – Meets BCBS 239 RDA requirement for flexible, timely reporting 25

Regulatory Reporting Current State ? Reports (forms) FORMS REPORTING ENTITY FORMS REGULATORY AUTHORITY 26

Regulatory Reporting Current State ? Reports (forms) FORMS REPORTING ENTITY REGULATORY AUTHORITY Change in Reporting requirements = Uncertainty • • Redevelopment effort Content of the reports • By each reporting entity • Are we comparing like with like? • For each system and form • Data quality and provenance 27

Regulatory Reporting with Semantics ! Ontology Granular Thing Contract IR Swap CDS Thing data Contract Bond IR Swap CDS Common ontology REPORTING ENTITY Bond REGULATORY AUTHORITY Data is mapped from each system of record into a common ontology Receives standardized, granular data aligned with standard ontology (FIBO) Reported as standardized, granular data Uses semantic queries (SPARQL) to assemble information Agnostic to changes in forms Changes to forms need not require reengineering by reporting entities 28

Regulatory Reporting with Semantics Conceptual Ontology Foundational semantics ! Ontology Granular Thing Contract IR Swap CDS Thing data Contract Bond IR Swap CDS Common ontology REPORTING ENTITY Bond REGULATORY AUTHORITY Data is mapped from each system of record into a common ontology Receives standardized, granular data aligned with standard ontology (FIBO) Reported as standardized, granular data Uses semantic queries (SPARQL) to assemble information Agnostic to changes in forms Changes to forms need not require reengineering by reporting entities 29

Ontology for Blockchain Page 30

Ontology for Blockchain • Physical API • • Conceptual Ontology (legal) Foundational semantics Page 31

Graph Analytics to Describe Risk Score = eigenvector centrality of adjacent network positions Line width reflects aggregate amounts at risk between individual trading parties SPARQL interface with R “igraph” package Node size reflects grand total aggregate amount at risk for entity 32

Use of Semantics Getting to There

Artifacts Analysis • See Onto. Clean (Guarino and Welty) • Other analysis frameworks – OOPS! – OQua. RE (derived from SQua. RE) – Ontology Hackathon unification of these • http: //ontologforum. org/index. php/Ontology. Summi t 2013_Hackathon_Clinics_FIBO_OOPS_OQua. RE • https: //docs. google. com/document/d/1 Erb. ZV 0 IFj 89 0 l. HFcnygsw 6 n 93 dxub 1 Aam. Ou 9 o. Bn. Hd. Oo/edit#head ing=h. kqnhdtvmz 5 vq 34

Information Technology and Data Knowledge and Meaning Deployment Roadmap Data Dictionary Physical Data Dictionary Is of

Knowledge and Meaning Deployment Roadmap Conceptual Information Technology and Data Linguistic Data Dictionary Is of

Knowledge and Meaning Deployment Roadmap Conceptual Information Technology and Data Linguistic Data Dictionary Is of

Knowledge and Meaning Deployment Roadmap Conceptual ontology Information Technology and Data By reference Data Dictionary Semantic Data Model Is of Or get rid of it! Is of Logical (Design) Ontology Application Ontology

Summary • Methodology: Understanding types of models – Semiotics (what the model represents) – Formalism (how it represents it) – Purpose (Logical v Conceptual) • • Truth is not meaning Syntax is not semantics Words play games Ontologies – Can be conceptual or “logical” (data focused) – Can have correspondence or foundational semantics • Conceptual ontologies use upper and cross domain ontologies – • Use the best of the research that is out there Applications – Different use cases suggest that different styles of ontology are most appropriate – Most use cases focus on data in some way and so need logical ontology style • Migration – no big bang needed; evolve from data ad dictionaries to concept ontology 39

Thank You! Mike Bennett mbennett@hypercube. co. uk www. hypercube. co. uk 40