Illustration - Classification

Since our announcement, a couple of weeks ago, we have received a lot of interest and numerous questions on how it works on a real case.

So, let’s take an example:

The BPI (Banque Publique d’Investissement) who, through our QIO (Questions>Insights>Outcomes) is asking us:
“What are the trends and how do compare those trends between Silicon Valley and the French Ecosystem for 6 topics: Artificial Intelligence, IoT, Transportation, Drones, Smartcity and Security.”

1/ The Encyclopedic recognition of Themes

Netvibes is using a personalized version of both DBPedia and DBPedia Spotlight, both open-sourced.

DBPedia structures the unstructured nature of Wikipedia to make it queryable. DBPedia Spotlights then annotate every single piece of content within the corpus aggregated in Cloudview (from Exalead, our sister brand at Dassault Systemes).

The aggregation process, to create and feed the corpus, is made of our own crawlers which will gather in permanence, content from personalized vertical libraries for Mainstream, Tech, Biz and Investment news. This way, we solely analyze noise-free content which is a fondamental requirement to get trustworthy content onto which we can build intelligence.

Immediate benefits of this Theme Recognition process:

a. Up-to-date encyclopedic annotations.
Corpuses are annotated with the biggest, most reliable and updated encyclopedia: Wikipedia.

b. Accurate disambiguation.
Using very rich and up-to-date profiling from Wikipedia means more accuracy the type of entity spotted.

c. Crowdsourced, international ontology.
Using customly improved ontologies open doors to more powerful filtering and smarter theme recognition.

2/ Drilling down to Insights

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