Topic Extraction with MeaningCloud and Graphileon

A short demo, illustrating how easy it is to integrate MeaningCloud's topic extraction service with Graphileon, and how you can create a graph with a document linked to the , automatically built hierarchy of concepts and entities.

Topic extraction is very often a crucial step in Natural Language Processing, It results in lists of entities, concepts, phrases, numbers that often are the input for further processes, used for Competitive Intelligence, Social Media Analysis and Search and Content Recommendation.  The items that are extracted are very diverse. Often they are also densely connected. These characteristics are in the sweet spot of graph databases, and more precisely in graph databases that use a property graph model, because these offer the possibility to add other that results from post-processing, like similarity coefficients, sentiment scores and user-ratings.

The video below illustrates how easy it is to integrate MeaningCloud’s topic extraction service with Graphileon, and how you can create a graph with a document linked to an hierarchy of concepts and entities that is automatically extended when new concepts or entities are found.

 

A more advanced application is explained in the video below. In addition to MeaningCloud, it uses  IA_FormView (based on jsonforms) , IA_AgGridView as well as the algorithm plugin from Neo4j.

 

 

This public diagram illustrates how the application is built. Click on a vertex or edge to look at its properties.
If you like this demo, we have also posted an article explaining how to set this up on your own Graphileon (Personal Edition) installation.

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Topic Extraction with MeaningCloud demo for your implementation

Following our article on topic extraction with MeaningCloud and Graphileon we had some users asking for instructions on how to build that application themselves. In this article we’ll describe the 2nd demo from that blog post, a Graphileon application which allows you add ‘Documents’ -pieces of text on a particular topic- which are then processed … Continued

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