In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
modul pembelajaran robotic Workshop _ by Slidesgo.pptx
GPT and Graph Data Science to power your Knowledge Graph
1. Neo4j, Inc. All rights reserved 2021
1
Workshop
● Get your Neo4j Engine up & running and register at:
https://neo4j.com/sandbox/
● Get the script to code (copy) along:
https://github.com/Kristof-Neys/Neo4j_demos
7. 7
20 / 20
Top US banks
3 / 5
Top Aircraft Manufacturers
7 / 10
Top Telcos
3 / 5
Top Hotel Groups
8 / 10
Top Insurance Companies
10 /10
Top Automakers
7 / 10
Top Retailers
5 / 5
Top Pharmaceuticals
Trusted by
75 of the
45. Neo4j, Inc. All rights reserved 2021
45
Demo Time…! (but first some
Cypher…)
46. Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
Cypher: first we CREATE
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MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} )
Person
NODE NODE
LABEL PROPERTY
LABEL PROPERTY
CREATE
RELATIONSHIP
name: ‘Ann’
LOVES
Person
name: ‘Dan’
47. Neo4j, Inc. All rights reserved 2021
Neo4j, Inc. All rights reserved 2021
Cypher: and then we MATCH a pattern in the Graph
47
MARRIED_TO
Person
name: ‘Dan’
MATCH (p:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
spouse
NODE
RETURN p, spouse
VARIABLE
48. Neo4j, Inc. All rights reserved 2021
48
In Cypher you MATCH a pattern and then RETURN a result
MATCH (c:Country {name: "Finland"})
RETURN c;
001
Filtering is done with WHERE (this statement does exactly the same)
MATCH (c:Country)
WHERE c.name = "Finland"
RETURN c;
002