Applying Standards to Information Centric Operations
• Derivatives Messaging 8 – headed up by Wells Fargo , this PoC showed how to map terms from the industry standard FpML messaging standard into the FIBO ontology , with automated classification of derivatives based on their properties .
• Regulation W ( Front-running ) [ 8 ] – headed up by Wells Fargo , this PoC introduced higher levels of logical representation to the ontology , to demonstrate automated compliance with US regulation W against front-running in securities trading . 9
• Derivatives 10 – headed up by State Street , this PoC demonstrated the use of FIBO in mapping and integrating derivatives data from various sources .
• Financial Reporting [ 9 ] – carried out at the Bank of England , demonstrating how use of a common ontology language could aid reporting .
These proofs of concept were highly successful in demonstrating the application of semantic technologies , including automated reasoning and semantic querying , for the target use cases .
However , by demonstrating the use of ontology for a single use case or a limited range of business scenarios , the primary benefit of ontology is not demonstrated in most of these PoCs , namely the ability to use the ontology as a common language across different sets of application data . The technology has been showcased very well , but the benefit of shared semantics for the most part has not .
Some of these initiatives focused unduly on words or data structures rather than real-world meaning . For example , modelers might take a common word , such as “ Affiliate ” in the Regulation W PoC , and try to account for most uses of the word even if these pointed to distinct concepts , leading to classes with optional properties . Addressing this has been considered out of scope for a proof of concept . There is then no guarantee that the same ontology could be applied in a different business scenario .
There is a distinction between the real-world features that give something its meaning , such as the legal capacities that define a legal person or a bank ( what we call “ truth makers ”), and data corresponding to those things , such as company registration numbers or banking licenses . These relationships were not always explicitly reviewed or documented in these proofs of concept .
8
Newman , D ., & Bennett , M . ( 2012 ). Semantic Solutions for Financial Industry Systemic Risk Analysis ( presentation ). Next Generation Financial Cyberinfrastructure Workshop , Robert H . Smith School of Business , University of Maryland . Available : https :// wiki . umiacs . umd . edu / clip / ngfci / images / 8 / 80 / BennettNewman . pdf
9
U . S . Electronic Code of Federal Regulations ( 2002 ). Available : https :// www . govinfo . gov / app / collection / cfr / 2002 /
10
David , E ., ( 3 June 2016 ). FIBO Marches Forward : A Look Inside State Street ' s FIBO Proof of Concept . Waters Technology . Available : https :// www . waterstechnology . com / data-management / 2459451 / fibomarches-forward-a-look-inside-state-streets-fibo-proof-of-concept
Journal of Innovation 17