Speakers: Alessandro Negro, Chief Scientist, GraphAware Abstract: In recent years, knowledge graphs are gaining significant traction across industries. This kind of graph excels uniquely at providing in-depth contextual knowledge so needed by organizations to successfully differentiate and compete today- particularly when leveraging unstructured data using NLP/U and Machine Learning on the graph. But where is the knowledge that is created from the combined sources? And how can we start surfacing new, high value insights that we could not obtain before? An example of how this can work is Hume, a graph-powered insights engine. It uses an innovative approach, creating what we call a Collaborative Knowledge Graph (CKG). In the CKG, Hume connects, enriches, and transforms data from structured and especially unstructured sources in order to liberate knowledge, discover hidden insights, and unlock new opportunities difficult or impossible to detect before. By ingesting and analyzing the details of your domain and the nuances of your most complex business challenges, and by enriching the CKG with external knowledge sources such as Wikidata, private knowledge bases and more, Hume shows one way to deliver on the value and potential of knowledge graphs.

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Using Knowledge Graphs
Alessandro Negro, Chief Scientist @ GraphAware
graphaware.com
@graph_aware, @AlessandroNegro
to predict customer needs,
improve product quality and
save costs

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Why am I receiving so many
complaints?
What can I propose to my customers
to make them happier?
Why are the production
costs increasing so much?
Where can my product chain
be improved?
Does a common pattern exist in all
these failures?

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Where would you start?

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From data to simulation
VALUE

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The Data Warehouse approach

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An OLAP on [multiple] OLTP
No events are stored neither relationships among them
KPIs are hard to evaluate
It is a data-driven decision making process!
The Data Warehouse approach

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Where is the knowledge?

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The Search Engine approach

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Documents are isolated
KPI based approach
It is hard to reveal common complex patterns
It is an information-driven decision making process!
The Search Engine approach

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Where is the knowledge?

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The Graph approach

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The Graph approach

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The Graph approach

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The Graph approach

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The Graph approach

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The Graph approach

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The Graph approach

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Knowledge Graph

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The Knowledge Graph approach
Entities are connected by nature
Multiple access patterns
A concrete enabler for Artificial Intelligence

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Ok, you convinced me.
How can I get started?

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Extract hidden structure from the text
Easily merge from different sources
Analysis framework
Visualisation tool
What should you look for?

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“GraphAware Hume converts structured
and unstructured data silos
to Knowledge Graph”

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Data Information Knowledge Insight Wisdom Simulation
Evolve
Enhancing the core
knowledge models that
drive an organization,
to derive ever greater
value.
06
Analyze
Finding and revealing
patterns in the
connections between
and amongst things.
03
Match
Finding and displaying
similarity between
things.
02
Act
Supplying solutions to
specific business and
strategy problems.
05
Communicate
Finding, extracting, and
executing the structure
of language.
04
Discover
Indexing, tagging,
filtering, sorting and
delivering listings.
01
VALUE

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Knowledge Graph store
Fast access to connected data
Merging External Data
Existing Data Augmentation
Scalability
The Neo4j role

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Demo Time

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Where is the knowledge?

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Thank you
For more information please contact:
Alessandro Negro · 10/01/2019
info@graphaware.com
+1 844-344-7274
graphaware.com