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Knowledge Graphs at Elsevier

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Paul Groth
Paul Groth

Pecha Kucha slides for 2015 World Wide Web Conference Industry Track panel on knowledge graphs in industry

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KNOWLEDGE GRAPHS AT ELSEVIER Paul Groth (@pgroth) Disruptive Technology Director labs.elsevier.com Knowledge Graph Industry Panel WWW 2015 http://www.www2015.it/industry-track/
Search is entity based
http://xkcd.com/605/
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier
Knowledge Graphs at Elsevier

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Knowledge Graphs at Elsevier

Notas del editor

  1. data integration has been a central challenge for companies for a long time
  2. We do data integration but for single domains
  3. It’s already useful to identify and categorize entiies but now we need a global view. Indeed we have a hand curated taxonomies
  4. 1. Translational queries
  5. 2. Search is entity centric
  6. 3. People have become more central with the rise of the Social graph and the importance of personalization and profiles
  7. 4. Applications are data driven, we need more and higher quality data predictive models
  8. 5. Applications are data driven, extensible APIs
  9. The flexible style of knowledge graphs
  10. Vast fields of unstructured data is the norm
  11. Effort to consume this data, arranging, input checking
  12. Ever more data,
  13. We can’t do all this by hand but we need to use the human annotators we do have more effectively especially when they are experts
  14. Knowledge graphs make it easier to do automated extraction by having a reference point and framework for doing and managing such extractions and putting those puzzle pieces together
  15. And also provides a powerful source of data that our monster models love
  16. Helps solve two key challenges: data integration in a flexible way and most of the methods that we do help us acquire data Now our there are a couple of things to worry about in this idealic view
  17. Organic – data quality is a central issue with this
  18. I think a powerful thing is the evolution of these knowledge graphs as they grow and change overtime