GraphConnect

In 2016, 25% of web searches on Android were made by voice and this percentage is predicted to double by 2018.

From Amazon Alexa to Google Home, smartwatches and in-car systems, touch is no longer the primary user interface.

In this talk, Alessandro and Christophe will demonstrate how graphs and machine learning are used to create an extracted and enriched graph representation of knowledge from text corpus and other data sources.

ThisSuch representation will then be used to map user intents made by voice to an entry point in this Neo4j backed knowledge graph.

Every user interaction will then have to be taken into account at any further steps and we will highlight why graphs are an ideal data structure for keeping an accurate representation of a user context in order to avoid what is called machine or bot amnesia.

The speakers will then conclude the session by explaining about how recommendations algorithms are used to predict next steps of the user's journey.