1. Presented by Grant Paton-Simpson
Word Puzzles with
Neo4j and Py2neo

2. Overview
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Brief look at graph databases & Neo4j
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Introduction to word transformation game
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Getting suitable words
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Adding words and relationships into Neo4j
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Querying graph data to generate puzzles

3. Graph Databases – a NoSQL option
http://neo4j.com/books/graph-databases/

4. NoSQL – when is it a good fit?
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SQL has its origins in the 1970s
and may not be fresh and shiny
any more but ...
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… we shouldn't choose NoSQL
for reasons of fashion.
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Venerable SQL often a better
choice for standard hierarchies
e.g. countries that have cities
that have suburbs etc

5. https://twitter.com/edd/status/400190499585544192

6. Graph Databases
●
Graph databases much, much better for related data with:
– lots of different links between same nodes
– different numbers of links between nodes
e.g. 3 hops to one peer and 7 hops to another
– lots of peer-to-peer links

7. Substantial Benefits
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Massive performance benefits (going exponential as number
of links grows)
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Structural harmony
– between structure of data and structure of data storage
(what you draw on the whiteboard might look very similar
to how you data is actually structured)
– between questions of data and query language used to
answer them

8. Word transformations
●
Start with one word and get to
the other by single-letter
tranformations word-by-word
●
E.g. starting with “stores” get to
“slaked”
– BTW there are 96 alternative
ways 5 moves or less
stores
stored
stared
staked
slaked

9. Puzzle taster
Get from 'sloven' to 'closed' in
no more than 5 steps
(there are 10 unique solutions)
sloven
?
closed

10. Getting a simple word list
●
How hard could it be?
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Lesson #1 – scrabble lists and similar are useless – only want lists
with standard words otherwise puzzles too hard
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Lesson #2 – have to decide about taboo/profane words
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Lesson #3 – the number of words affects the number of
ONE_LETTER_DIFF relationships a lot
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Lesson #4 – clever optimisation not needed if restricting self to
ordinary words
SCOWL (Spell Checker Oriented Word Lists) http://wordlist.aspell.net/

11. Filtering words
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Needed to turn é to e
●
Needed to eliminate possessives e.g. cat's (as used in the phrase “the
cat's whiskers”)
●
Needed to leave out capitalised words

12. For each word, identifying words different
by one letter only
Disclaimer: the code worked but probably some super-smart optimisations
would be possible involving n-dimensional space or something

13. Adding data to Neo4j
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Create nodes and relationships
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Lots of room for optimisations
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Only need to build database once so 15 minutes is not worth
reducing
●
My Neo4j and Py2neo is beginner level but I was able to solve my
problem

14. Py2neo and Cypher

15. Cypher Syntax as ASCII Art (Really!)
Word Word
ONE_OFF
(Word) -[ONE_OFF]->(Word)

16. Cypher Syntax as ASCII Art (Really!)
Word Word
ONE_OFF
(Word) -[ONE_OFF]->(Word)
How cool is this?

17. Example Output

18. Matching chart

19. Live Demo – Suggestions for Start Word

20. “sloven” to “closed” solution(s)

21. Resources
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Neo4j
– http://neo4j.com/books/graph-databases/
– http://neo4j.com/graphacademy/
– http://graphgist.neo4j.com/#!/gists
– https://www.youtube.com/channel/UCvze3hU6OZBkB1vkhH2lH9Q
●
Py2neo
– http://py2neo.org/2.0/
●
SCOWL
– http://wordlist.aspell.net/

22. About Catalyst