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

2.
Overview
●
Brief look at graph databases & Neo4j
●
Introduction to word transformation game
●
Getting suitable words
●
Adding words and relationships into Neo4j
●
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?
●
SQL has its origins in the 1970s
and may not be fresh and shiny
any more but ...
●
… we shouldn't choose NoSQL
for reasons of fashion.
●
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
●
Massive performance benefits (going exponential as number
of links grows)
●
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?
●
Lesson #1 – scrabble lists and similar are useless – only want lists
with standard words otherwise puzzles too hard
●
Lesson #2 – have to decide about taboo/profane words
●
Lesson #3 – the number of words affects the number of
ONE_LETTER_DIFF relationships a lot
●
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
●
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
●
Create nodes and relationships
●
Lots of room for optimisations
●
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
●
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