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Using Knowledge Graphs to Predict Customer Needs, Improve Product Quality and Save Costs

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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.

Publié dans : Technologie
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Using Knowledge Graphs to Predict Customer Needs, Improve Product Quality and Save Costs

  1. 1. © 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
  2. 2. © 2019 GraphAware Ltd. All rights reserved.
  3. 3. © 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?
  4. 4. © 2019 GraphAware Ltd. All rights reserved. Where would you start?
  5. 5. © 2019 GraphAware Ltd. All rights reserved. From data to simulation VALUE
  6. 6. © 2019 GraphAware Ltd. All rights reserved. The Data Warehouse approach
  7. 7. © 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
  8. 8. © 2019 GraphAware Ltd. All rights reserved. Where is the knowledge?
  9. 9. © 2019 GraphAware Ltd. All rights reserved. The Search Engine approach
  10. 10. © 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
  11. 11. © 2019 GraphAware Ltd. All rights reserved. Where is the knowledge?
  12. 12. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  13. 13. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  14. 14. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  15. 15. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  16. 16. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  17. 17. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  18. 18. © 2019 GraphAware Ltd. All rights reserved. The Graph approach
  19. 19. © 2019 GraphAware Ltd. All rights reserved. Knowledge Graph
  20. 20. © 2019 GraphAware Ltd. All rights reserved. The Knowledge Graph approach Entities are connected by nature Multiple access patterns A concrete enabler for Artificial Intelligence
  21. 21. © 2019 GraphAware Ltd. All rights reserved.
  22. 22. © 2019 GraphAware Ltd. All rights reserved. Ok, you convinced me.
 How can I get started?
  23. 23. © 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?
  24. 24. © 2019 GraphAware Ltd. All rights reserved. “GraphAware Hume converts structured and unstructured data silos
 to Knowledge Graph”
  25. 25. © 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
  26. 26. © 2019 GraphAware Ltd. All rights reserved.
  27. 27. © 2019 GraphAware Ltd. All rights reserved. Knowledge Graph store Fast access to connected data Merging External Data Existing Data Augmentation Scalability The Neo4j role
  28. 28. © 2019 GraphAware Ltd. All rights reserved. Demo Time
  29. 29. © 2019 GraphAware Ltd. All rights reserved. Where is the knowledge?
  30. 30. © 2019 GraphAware Ltd. All rights reserved. Thank you For more information please contact: Alessandro Negro · 10/01/2019
 info@graphaware.com +1 844-344-7274 graphaware.com

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