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Max De Marzi, is a seasoned web developer. He started building websites in 1996 and has worked with Ruby on Rails since 2006. The web forced Max to wear many hats and master a wide range of technologies. He can be a system admin, database developer, graphic designer, back-end engineer and data scientist in the course of one afternoon. Max is a graph database enthusiast. He built the Neography Ruby Gem, a rest api wrapper to the Neo4j Graph Database. He is addicted to learning new things, loves a challenge and finding pragmatic solutions. Max is very easy to work with, focuses under pressure and has the patience of a rock. Max is a DZone MVB and is not an employee of DZone and has posted 60 posts at DZone. You can read more from them at their website. View Full User Profile

Hunting Trolls with Neo4j

10.07.2012
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Allison Sparrow shared a link to Patentula, a company interested in finding better ways to explore patent data and hunt patent trolls. What caught my attention is this quote from the video below:

What we tried to do with it, is bypass any sort of keyword processing in order to find similar patents. The reason we’ve done this is to avoid the problems encountered by other systems that rely on natural language processing or semantic analysis simply because patents are built to avoid detection by similar keywords…we use network topology (specifically citation network topology) to mine the US patent database

in order to predict similar documents.


When dealing with a large text dataset, most folks jump right into NLP and semantic analysis, it’s interesting to learn when that’s not such a good idea.

Check out the full video:

 

… and if you wanted to see just how exciting troll hunting can be: 

>8-]

 

 

 

Published at DZone with permission of Max De Marzi, author and DZone MVB. (source)

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