natural language search with neo4j - kenny bastani @ graphconnect ny 2013

Natural Language Search...using Neo4j

We’ll be covering...What is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

What is Natural Language Search?

Natural language search is like querying a database using your own natural language.

In a way, it is kind of like programming a person with words (Teaching, Evangelism, Sales Pitches, Planning, etc.)

ProgressWhat is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

What do brains and graphs have in

common?Networks condense a lot of information into small points.

These small points help us understand or interpret a lot of information by exploring the world from many different small points.

Graphs, like brains, help us explore a lot of information from relative points.

But what is a network?A network is a representation or model

of the interconnectedness of information.

A graph is the de facto mathematical component that defines the level of interconnectivity in a network.

A graph database merges these two concepts into a persistent storage medium.

Networks (Information) + Graph (Mathematics) = Neo4j

Graph of people meeting people

Anne met Pam

Pam met Sally

Sally met Anne

John met Sally

Path Finding = Searching

The key component when using a graph database is traversals.

Traversals model the pathways in a network by enumerating over all possibilities.

Possibilities that meet a criteria are returned by a query.

(Neo4j’s Cypher Query Language)

ProgressWhat is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

Time based traversals

Time is a hierarchical method of categorizing the linearity of global events.

Hours, minutes, seconds...

“Neo4j Meetup is at 6:00 PM on October 29th”

Time Scale Event Meta Model

Modeling events over time is easy in Neo4j

Let’s go over the GraphGist for the Time Scale Event Meta Model

http://gist.neo4j.org/?github-kbastani%2Fgists%2F%2Fmeta%2FTimeScaleEventMetaModel.adoc

ac

Progress

What is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

ProgressWhat is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

Neo4j allows you to store information as a series of paths, and that is really valuable for giving a user options when it comes to search.

It starts with something I call

“Search Cache”

Search CacheA search cache is a repository of all relevant paths condensed into a hierarchical data store.

A hierarchical data store is like folder paths that model a storage collection into a linear path. (Dimensionality Reduction)

An address is a hierarchy, revealing a path.

ex. http://www.neo4j.com/download

ex. > root\neo4j-community\bin\neo4j.sh

Natural language path:

> w\h\a\t\ \i\s\ \t\h\e\ \m\a\t\r\i\x\?

Type Ahead / Autocomplete

For search it comes down to enumerating over all possibilities and then mapping those paths to an action.

http://kbastani.github.io/predictive-autocomplete

Never do real time processing for natural language search (It is a hard problem -- which means it will take time*)

Distributed Caching Frameworks

Take a distributed approach to building out your search cache.

Use Neo4j to model your network and then enumerate over all possibilities as a query and add each possibility to a search cache.

Distribute the load to a network of compute instances like MapReduce.

In C# at http://kbastani.github.io/predictive-autocomplete

How do I build a search cache?

The best way to do this is using blob storage.

I use Windows Azure, but you can use any data storage as long as it maps to a JSON file via HTTP GET request.

ex. HTTP GET

../natural/language/search/is/cool

.. Working on open source project using C#

ProgressWhat is natural language search?

What do brains and graphs have in common?

How do you model time as a graph?

How do you model time-based events on a graph?

How do you anticipate natural language queries and map those to results?

How do you transform answers into questions?

How to transform answers into questions?

You have a bunch of answers already in natural language.

Each language has a specific template that allows you to transform an answer into a question.

“X is Y” -> “What is X?”

Is X a Person? Then “Who is X?”

Add “What is X?” to the search cache.

Example: http://www.arktera.com/

Questions?

MATCH questions-[:without]->answers

RETURN *

0 Results Found

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