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

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).  Copyright © 2010 EDM Council Inc.

Financial Industry Business Ontology (FIBO) 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 is built on state-of-the-art collaboration technology and supported by documented and tested governance FIBO is expressed in the W3C standard (RDF/OWL) for flexible and scenario-based/inference analysis FIBO provides structural validation to ensure completeness, consistency and allowable values FIBO feeds analytical processes with trusted data and powers smart contracts

Debt Securities Sample Hierarchy: Bonds

Corporate Bonds Overview

Examples: Bonds Foundational concepts for Contracts 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 are also properties defined in terms of simple assertions (yes/no, text, numbers) Also show what kind of thing something is (classification hierarchy) Term Definition Synonyms Necessary Conditions? bond 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.   has principal repayment terms Repayment terms for the principal on a bond has redemption terms must be some bond principal repayment terms set Terms for the repayment of the Principal on a Bond security.

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”

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

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

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

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

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

Ontology to Data Model

Data Model to Ontology

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

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 Reference ontology Legacy Data Sources and Systems Reporting Risk, Compliance etc. Semantic Queries Reference ontology R2RML based Ontology to Legacy Database Adapters Legacy Data Sources and Systems

Virtual Ontology Reference ontology Legacy Data Sources and Systems Logical Ontology Foundational semantics Reporting Risk, Compliance etc. Semantic Queries Reference ontology R2RML 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

Semantic Web Applications Semantic Operational Processing Reasons over Data to Infer Classifications and Relationships isTradingWith is a new property relationship that is inferred based on a semantic rule and can be queried An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments. Human Facing Definition Swap_Contract and hasLeg FixedRateLeg and hasLeg FloatingRateLeg Machine Facing Definition Fixed Float IR Swap (Ontology) isTradingWith Business Entity Business Entity Interest Rate Swap Acme Inc Trader LLC 4 Semantic reasoning type Inferred 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 identifies Fixed Float IR Swap identifies type Inferred LEI5001 Swap1001 LEI7777 LEI hasLeg party hasLeg Swap party LEI 3 Semantic reasoning FloatingRateLeg Inferred Leg 1 Leg 2 Inferred FixedRateLeg type type notional index fixedRate notional Leg1 is inferred to be a FloatingRateLeg because any leg tied to an index is semantically defined as floating 10000000 LIBOR 3.5% 10000000 Leg2 is inferred to be a FixedRateLeg because any leg tied to an interest rate is semantically defined as fixed currency currency USD Data for an undefined Swap Contract before semantic reasoning performs classification and identification 1 Semantic reasoning USD 2 Semantic reasoning David Newman, Wells Fargo

Semantic Web Applications Physical Ontology Internal Consistency semantics (reasoning) isTradingWith is a new property relationship that is inferred based on a semantic rule and can be queried An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments. Human Facing Definition Swap_Contract and hasLeg FixedRateLeg and hasLeg FloatingRateLeg Machine Facing Definition Fixed Float IR Swap (Ontology) isTradingWith Business Entity Business Entity Interest Rate Swap Acme Inc Trader LLC 4 Semantic reasoning type Inferred 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 identifies Fixed Float IR Swap identifies type Inferred LEI5001 Swap1001 LEI7777 LEI hasLeg party hasLeg Swap party LEI 3 Semantic reasoning FloatingRateLeg Inferred Leg 1 Leg 2 Inferred FixedRateLeg type type notional index fixedRate notional Leg1 is inferred to be a FloatingRateLeg because any leg tied to an index is semantically defined as floating 10000000 LIBOR 3.5% 10000000 Leg2 is inferred to be a FixedRateLeg because any leg tied to an interest rate is semantically defined as fixed currency currency USD Data for an undefined Swap Contract before semantic reasoning performs classification and identification 1 Semantic reasoning USD 2 Semantic reasoning David Newman, Wells Fargo

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 BCBS239 RDA requirement for flexible, timely reporting

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

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

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

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

Ontology for Blockchain

Ontology for Blockchain Conceptual Ontology (legal) Foundational semantics Physical API

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

Getting to There Use of Semantics

Artifacts Analysis See OntoClean (Guarino and Welty) Other analysis frameworks OOPS! OQuaRE (derived from SQuaRE) Ontology Hackathon unification of these http://ontologforum.org/index.php/OntologySummit2013_Hackathon_Clinics_FIBO_OOPS_OQuaRE https://docs.google.com/document/d/1ErbZV0IFj890lHFcnygsw6n93dxub1AamOu9oBnHdOo/edit#heading=h.kqnhdtvmz5vq

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

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

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

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

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