Weaviate is a smart graph that acquires and integrates information into an ontology and applies reasoning to derive new knowledge. It allows for semantic search, automatic classification, and knowledge representation. Weaviate has features like contextionary, peer-to-peer networking, GraphQL, containerization, and scalable HTTP APIs to enable developers to build applications.

1. IntroductiontotheWeaviateSmartGraph
@bobvanluijt

2. What’stheproblem?

3. 3

4. 4
SELECT * FROM `actors` WHERE `name` = ‘Brad Pitt’

5. 5
Brad Pitt ?
is a(n)

6. 6WikiMedia

7. 7Getty Images

8. 8
A Bain & Company survey of 325 global companies found that;
1. 59% believe they lack the capabilities to generate
meaningful business insights from their data.
2. 85% said they will require substantial investments
to update their data platform.

9. What’sthesolution?

10. 10
Smart Graphs

11. 11
A [smart]graph acquires and integrates
information into an ontology and applies a
reasoner to derive new knowledge.
“
Towards a Definition of Knowledge Graphs

12. 12

13. Howtocreateyourown?

14. 14

15. 15
Weaviate Smart Graph

16. 16
Apps
etcetera

17. UseCaseFeatures

18. 18
Semantic Search

19. 19
Automatic
Classification

20. 20
Knowledge
Representation

21. DeveloperFeatures

22. 22
Contextionary
P2P Network
GraphQL Containerized
ScalableHTTP API

23. Weaviate’sContextionary

24. 24

25. 25
company
apple
fruit
[0.34534, 2.3543, 8.34532 … ]
600 dimensions
stanley kubrick
2001 a space odyssey

26. 26
Company, name, Apple,
founded, in, 1976, in,
country, with, the, name,
United, States
Company:
name: Apple
foundedIn: 1976
inCountry:
"$cref": ...
hasCeo:
Person:
name: Tim Cook

27. 27
Company, name, Apple,
founded, in, 1976, in,
country, with, the, name,
United, States
[0.34534, 2.3543, 8.34532 … ]

28. 28

29. 29
company
iphone

30. 30
{
Get{
Things{
Company(explore:{
concepts: ["fashion"]
}){
name
}
}
}
}

31. 31
{
"data": {
"Local": {
"Get": {
"Things": {
"Company": [
{
"name": "Vogue"
}
]
}
}
}
},
"errors": null
}

32. Demo

33. Usecaseexamples

34. Retail & Consumer Goods
34
Description: Find customer white spots in public
datasets.
Triggers: manual search in cities.
Business case: new customers are found manually,
Weaviate automates this which saves money.
USPs used: knowledge representation and semantic
search.

35. Cyber Security
35
Description: Match cybersecurity notifications to
threads.
Triggers: Cybersecurity threads are often matched
manually.
Business case: Cybersecurity threads are often
matched manually, Weaviate automates this which
is faster and cheaper.
USPs used: Automatic classification

36. Oil & Gas
36
Description: relate thousands of databases.
Triggers: different databases uses different
language to describe the same thing.
Business case: automatically find the location of
data without manual intervention.
USPs used: automatic classification and knowledge
representation.

37. www.semi.technology

38. Thankyou!
www.semi.technology
bob@semi.technology
@bobvanluijt