Building a Tiny Knowledge Graph with BERT and Graph Convolutions.

on point! Knowledge from Text gets more overview in graph!
HOW TO ? this block is telling the story

Cloud Computing For Science and Engineering

Abstract

Knowledge graphs (KGs) have become an important tool for representing knowledge and accelerating search tasks. Formally, a knowledge graph is a graph database formed from entity triples of the form (subject, relation, object) where the subject and object are entity nodes in the graph and the relation defines the edges. When combined with natural language understanding technology capable of generating these triples from user queries, a knowledge graph can be a fast supplement to the traditional web search methods employed by the search engines. In this tutorial, we will show how to use Google’s Named Entity Recognition to build a tiny knowledge graph based on articles about scientific topics. To search the KG we will use BERT to build vectors from English queries and graph convolutions to optimize the search. The result is no match for the industrial strength KGs from the tech giants, but we hope it helps…

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