Knowledge graphs are on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries, from fraud detection and chatbots, to risk analysis and recommendation engines. In this webinar we dive into key technical and business considerations, use cases and best practices in leveraging knowledge graphs for better knowledge management.
Knowledge Graph Discussion: Foundational Capability for Data Fabric, Data Integration and Analytics
1. Sean Martin - Founder & CTO
Ben Szekely - SVP and Co-founder, Head of Field Operations
July 2020
Knowledge Graph Discussion:
Foundational capability for data fabric, data
integration and analytics.
6. Knowledge Graphs and Data Complexity
Problem
● Deliver a final set of data derived from historical clinical trials and describe
the methods used to create it
Source Data
● 88,000 SAS files for 2,792 studies
● Reference data and mapping definition
Data Metrics
● 2.6M variables identified
● 678K unique subject ids extracted
● 162M records onboarded
● 15B RDF Triples in the Knowledge Graph
Approach
● 2 weeks - Metadata analytics, automated mapping, and
in-memory transformation rules to unify data to the SDTM
model
10. Why Knowledge Graphs and Ontologies?
Simplifies access to complex data to address
unanticipated questions
Quickly profiles, connects and harmonizes data
from multiple sources, including unstructured
Presents tailored views, services and experiences
to different personas with conceptual models
Flexibly accommodates new data sources
and use cases on the fly, with minimal impact
Scales horizontally to accommodate enterprise
data fabric scale - Cloud agnostic
12. Anzo’s Graphmarts to offer three ways to
work with Knowledge Graphs in-memory
1. Load onboarded RDF graph data
from disk
2. Load data into memory directly from
sources, APIs, streams
3. Load data at query-time through
virtualized views
Roadmap: Towards Virtual Knowledge Graphs