3. Accelerating ML Ops with Graphs and Ontology-Driven Design

25 Jul, 2022



Speakers:

• Brandon Campbell, Author, Ontologist and Software Engineer
• Joel Linford, SDS Digital Innovations Lead Data Scientist, Northrop Grumman
Session type: Full Length Session

Abstract: Data fusion is a prerequisite to high-leverage analytics, but multi-source integration into data lakes becomes incomprehensible at scale. Data lakes collocate data but do not create synergy because they lack structure and context. When the time comes to engineer features, data lakes do not provide a means to maintain digital threads. The burden of preserving context falls to users, who pass tribal knowledge from one to the next through word of mouth or documentation. This process creates bottlenecks in data processing and analytics, resulting in loss of clarity over time. To overcome these challenges, Ontology-Driven Design operates on the premise that data integration should be governed by knowledge. In this paradigm, domain knowledge is modeled ontologically, which kills two birds with one stone. Firstly, the domain knowledge serves as an integration layer for disparate data. Secondly, the combination of data and ontology results in a context-rich graph that preserves domain knowledge in a digital thread. In this talk, we demonstrate how Northrop Grumman uses Neo4j graph databases to realize ODD pipelines that generate knowledge graphs can then be supercharged through analytical methods to turn data and domain knowledge into customer value.

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