Applying FIBO and Knowledge Graphs in the Financial Industry - Day 1
  Michael Atkin   Michael Atkin
Strategic Advisor
EDM Council
  Dean Allemang   Dean T Allemang
Ontologist, Author, Semantic Web Consultant
Working Ontologist, LLC
  Dennis Wisnosky   Dennis Wisnosky
Former Chief Architect and CTO for the US DoD Business Mission Area/Lead FIBO Development Engineer
Wizdom Systems, Inc.
  Jacobus Geluk   Jacobus Geluk
Former Head of the Enterprise Knowledge Architecture Team at BNY Mellon
EDM Council
  David Newman   David S. Newman
SVP, Strategic Planning Manager Innovation R&D
Wells Fargo


Wednesday, October 10, 2018
08:00 AM - 05:00 PM

Level:  Intermediate

Semantic Technology: FIBO Tutorial on Applying Knowledge Graphs in the Financial Industry

Event: October 10-11, Chicago (in conjunction with the Data Architecture Summit)
Content: 12 hours

  1. Ontology Rationale - using ontologies and standards to address data incongruence
  2. Knowledge Graph Primer - understanding the core technical concepts needed for model-driven architecture (RDF, RDFS, SKOS, OWL) and query using SPARQL
  3. FIBO - leveraging the structure and content of the Financial Industry Business Ontology
  4. Collaboration using the FIBO Ecosystem - GitHub, JIRA, Jenkins, and the FIBO Wiki system

Ontology Rationale (90 minutes)

  • Data as Meaning - Data is a representation of “real things” that can be described via precise identification, verifiable facts, and contextual relationships.
  • Agreements and Obligations - Complexity requires shared meaning.
  • Causes and Implications - the underlying reasons why data loses alignment
  • Demystifying Ontology - moving from “location-based” to “meaning-based” processing
  • Enterprise Knowledge Graph - the value of a common reusable Open World model using semantic technology

Knowledge Graph Primer (3 hours)

  • Enterprise Knowledge Graphs - organizing and linking content across federated environments
  • Ontology Languages - understanding RDF, RDFS, SKOS, OWL, and SPARQL
  • Inferencing - the basics of description logic, classes, properties, and restrictions
  • Implementation - constructing the graph and linked open data

Financial Industry Business Ontology (6 hours)

  • FIBO Product Suite - review of FIBO Glossary, Data Dictionary, FIBO Vocabulary, Linked Data Fragments, Visual Ontology, UML models, OWL files, and
  • FIBO Maturity Levels - the distinctions between FIBO production and development
  • Semantic Architecture - triple store processing, namespaces, and ontologies
  • FIBO Concepts - understanding the classes, properties, and logical assertions within FIBO
  • Conversion and Mapping - transforming CSV, XML, and relational databases into RDF to establish the conceptual mapping baseline
  • Accessing FIBO - tools for reading, editing, extending, and embedding FIBO into applications
  • Working with FIBO - URIs, namespaces, instances, property chains, and simple SPARQL queries
  • Inferencing Basics - tools and techniques for implementing the inference rules within FIBO

The FIBO Ecosystem (90 minutes)

  • Build, Test, Deploy, Maintain – understanding the FIBO development process
  • FIBO Ecosystem – using GitHub and JIRA as the mechanisms for FIBO collaboration

Mike is a strategic advisor to the Enterprise Data Management Council - a business forum focused on data content management, best practices, ontology, and standards in the financial industry. Mike is recognized as an expert in data management and has been providing strategic advice to financial industry participants since 1985. He served as a member of the SEC Market Data Advisory Committee, the CFTC Technical Advisory Committee, various ISO Working Groups, and the Financial Stability Board’s Advisory Group for LEI. He was Chair of the Data and Technology Subcommittee for the US Treasury’s Financial Research Advisory Committee. For the past three years, Mike has been on the faculty of Columbia University teaching master’s degree candidates about the principles, practices, and operational realities of data management.

Dean Allemang is the author of the leading textbook on Semantic Technologies ("Semantic Web for the Working Ontologist"). Dean has trained over 1,000 professionals on the application and deployment of Semantic Web technology. He is an experienced Data Management consultant and trainer in Semantic Technologies, and sits on the design committees of several major international standards bodies.

Dean currently directs Working Ontologist, LLC, a consultancy that specializes in the development and deployment of semantic web solutions. His current industry focus is in finance, media, agriculture, and life sciences. 

Dennis Wisnosky provides technical strategy and operational guidance to the EDM Council with regard to finalizing and implementing the FIBO standards. Dennis brings extensive experience of enterprise architecture, ontology development, and semantic web technology from his former role as the Chief Technology Officer and Chief Architect in the U.S. Department of Defense Business Mission Area, within the Office of the Deputy Chief Management Officer and his many years of system development and deployment in the private sector.

David provides leadership and expertise for the advancement of knowledge graph and machine-intelligence-based technologies at Wells Fargo. His team develops innovations that employ various AI capabilities, including semantic technology, graph analytics, machine learning, and natural language processing. David’s core mission is to use AI to develop the foundational building blocks for the future of data at Wells Fargo. David’s interests include how to operationally leverage knowledge graphs to support data catalogs, semantic data lakes, and as a source to train machine learning algorithms.

David chairs the Financial Industry Business Ontology (FIBO) initiative, a collaborative effort of global banks, financial regulators, and vendors, under the auspices of the Enterprise Data Management Council, to semantically define a common language standard for finance using ontologies. David is also engaged in a collaborative effort with academic researchers to use knowledge graphs to help explain the influential features contributing to machine learning predictions.