Using Knowledge Graphs to Predict Customer Needs and Improve Quality

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1 like•582 views

The document discusses using knowledge graphs to gain insights from data by revealing patterns and relationships. It describes current approaches like data warehouses and search engines that treat data as isolated and don't capture connections. The key advantage of a knowledge graph is that entities are naturally connected, allowing for multiple access patterns and enabling artificial intelligence. Building a knowledge graph involves extracting structure from various sources and providing tools for analysis and visualization.

Software

© 2019 GraphAware Ltd. All rights reserved.
Using Knowledge Graphs
Alessandro Negro, Chief Scientist @ GraphAware
graphaware.com
@graph_aware, @AlessandroNegro
to predict customer needs,
improve product quality and
save costs

© 2019 GraphAware Ltd. All rights reserved.

© 2019 GraphAware Ltd. All rights reserved.
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?

© 2019 GraphAware Ltd. All rights reserved.
Where would you start?

© 2019 GraphAware Ltd. All rights reserved.
From data to simulation
VALUE

© 2019 GraphAware Ltd. All rights reserved.
The Data Warehouse approach

© 2019 GraphAware Ltd. All rights reserved.
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

© 2019 GraphAware Ltd. All rights reserved.
Where is the knowledge?

© 2019 GraphAware Ltd. All rights reserved.
The Search Engine approach

© 2019 GraphAware Ltd. All rights reserved.
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

© 2019 GraphAware Ltd. All rights reserved.
The Graph approach

© 2019 GraphAware Ltd. All rights reserved.
Knowledge Graph

© 2019 GraphAware Ltd. All rights reserved.
The Knowledge Graph approach
Entities are connected by nature
Multiple access patterns
A concrete enabler for Artiﬁcial Intelligence

© 2019 GraphAware Ltd. All rights reserved.
Ok, you convinced me. 
How can I get started?

© 2019 GraphAware Ltd. All rights reserved.
Extract hidden structure from the text
Easily merge from diﬀerent sources
Analysis framework
Visualisation tool
What should you look for?

© 2019 GraphAware Ltd. All rights reserved.
“GraphAware Hume converts structured
and unstructured data silos 
to Knowledge Graph”

© 2019 GraphAware Ltd. All rights reserved.
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

© 2019 GraphAware Ltd. All rights reserved.
Knowledge Graph store
Fast access to connected data
Merging External Data
Existing Data Augmentation
Scalability
The Neo4j role

© 2019 GraphAware Ltd. All rights reserved.
Thank you
For more information please contact:
Alessandro Negro · 07/05/2019 
info@graphaware.com
+1 844-344-7274
graphaware.com

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Romain CAMPOURCY – Architecte Solution, Sopra Steria Patrick MEYER – Architecte IA Groupe, Sopra Steria La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.

ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...

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Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin) Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.

GraphSummit Paris - The art of the possible with Graph Technology

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GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

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Dr. Sean Tan, Head of Data Science, Changi Airport Group Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.

GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...

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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

Neo4j

GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...

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Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.

GraphAware - Transforming policing with graph-based intelligence analysis

Neo4j

Petr Matuska, Sales & Sales Engineering Lead, GraphAware Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.

GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates

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KLARNA - Language Models and Knowledge Graphs: A Systems Approach

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INGKA DIGITAL: Linked Metadata by Design

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Your enemies use GenAI too - staying ahead of fraud with Neo4j

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Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j. Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.

BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx

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Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.

Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan

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