A couple of days ago, I wrote a graphgist about creating a true Knowledge Graph for the Neo4j ecosystem. Based on the fantastic
Awesome Neo4j resource created by our friends at
Neueda/
Neueda4j. You can access it in a separate window
over here.
In this post however, I will go into a bit more detail about how I went about creating that graph.
Google Spreadsheet is my friend
I mentioned already that I started from the awesome
Awesome Neo4j github resource. And while it's a great idea to manage pages etc collaboratively on Github, I can't help but feel like there should be other and nicer ways of structuring that information. So I spent a couple of hours converting that information into a spreadsheet (which is
publicly accessible over here):
This sheet contains
- info about the resource (name and comments)
- the URL where you can find the resource
- info about the author (individual or organisation) that created/manages the resource
So it's a very, very easy graph model:
So all I needed to do was import that sheet into Neo4j. Easy...
Importing the Google Spreadsheet with Load CSV
As we know by know, it's really easy to
download a Google spreadsheet as a CSV file, and then it is pretty darn easy to import that CSV into Neo4j with
Load CSV. I have two versions of that load script:
The result is not a very big graph of course:
And now we can do some nice querying on it - just for fun!
Querying the Neo4j KnowledgeGraph
Obviously there are many different queries that we could run on an interesting graph like this. I have put a couple of them
on Github as well. Here they are:
//Find some Authors, Resources and Tags
MATCH p = ((a:Author)--(r:Resource)--(t:Tag))
return p
limit 25
Gives you an initial sample of the graph:
Then we can explore a couple of specific graph neighborhoods:
//Find some Authors, Resources and Tags connected to Rik or Max
MATCH (t:Tag)--(r:Resource)--(a:Author)
where a.name contains "Rik" or a.name contains "Max"
return t,r,a
this gets us this one:
And then we can also "recreate" a spreadsheet-like view of the graph:
//find some resources and authors
MATCH (r:Resource)--(a:Author)
where a.name contains "Rik" or a.name contains "Max"
return distinct a.name as Author, r.name as Resource, r.url as URL, r.comments as Description
order by Author;
This gets us (pitty that the url's don't get hyperlinked like they do on the graphgist):
And then finally, let's look at some pathfinding - always interesting:
//find some paths between books and blogs
match (t1:Tag {name:"book"}), (t2:Tag {name:"blog"}),
p = allshortestpaths ( (t1)-[*]-(t2))
return p
limit 10
So there you go. A first attempt at creating another graph-based knowledge repository for all things
Neo4j. Hope you guys enjoyed that. I know I did :))
Cheers
Rik
Labels: csv, google spreadsheet, graph database, graphgist, knowledge graph, neo4j
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