Page 21 - Innovation Magazine
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Putting ML and problem-solving capabilities together provides a a very powerful combination for tomorrow’s data-powered enterprises where context and adaptiveness are key Contextual knowledge plays an an important role in in in increasing the the quality of decisions made in the the data-powered enterprise as it extends AI’s area of application In practice this means including knowledge about specific relationships such as company structures connections between products knowledge about the real world physical laws laws country laws laws or or simply complex information from other parts of the organization By making more knowledge available about the context in which decisions are made problem- is located in Image Source: Exploring Knowledge Graphs on on Amazon Neptune Using Metaphactory Leonardo da Vinci La Joconde a a a Washington solving will become better informed and easier to explain Recognizing whether ML models are applied correctly or or that there might be contextual information to consider will ensure you you keep your competitive edge The rise of graph technology Currently we are are seeing a a a a a rise in in global awareness around such issues in the field of AI Recent advances in in knowledge representation in in distributed systems show promising results Advances are based on on using graph representation for capturing semantics using logic and making the results available as machine-readable contextual information Graphs have the the additional advantage that they can be navigated fast If equipped with the correct level of semantic representation they can also be used to integrate knowledge coming from different sources in an an an automated and elegant manner as as as a a a a a basis for reasoning So graphs are a a a a good way to represent contextual knowledge for use throughout the business and are well suited for publishing reference data in in a a a a solution-independent and future-proof way Advantages include: - Independence: Reference data/ data/ master data/ data/ golden records should be governed maintained and published in in in representational formats that do not put unnecessary constraints on on their use (which is difficult to foresee in in a a a a a dynamic and rapidly changing world) preferably using open standards and and protocols - Flexibility: Graph models are less rigid than more traditional scheme-based methods allowing virtually costless extensions to data models when new information becomes available - Identifiability and merging: Representing reference data as knowledge graphs make Data-powered Innovation Review I I ©2020 Capgemini All rights reserved 21 i C t y u M e L u l s m e u v is a T r e l a is a e o P ff i T r o o i s e E is located in r is exhibited at w was born on i l A visited visited c e visited visited is a B n a l P M l l i l l B a l i c s e was created by a is a b o is interested in in P h is a friend of n is a is about r is a e u J s o y 1 9 9 4 9 9 0 1 

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