Knowledge graphs (KGs) have become an important tool for representing knowledge and improving search results. Formally, a knowledge graph is a graph database formed from entity triples of the form (subject, relation, object) where the subject and object are 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 methods employed by the search engines. 

This short talk shows 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 a tiny demonstration that we hope helps to illustrate some core concepts.

Presenter Bio

Dr. Dennis Gannon, Professor emeritus in the School of Informatics and Computing at Indiana University

• Previous roles at Microsoft include directing research as a member of the Cloud Computing Research Group and the Extreme Computing Group
• Published over 200 scientific articles and 4 books, including Cloud Computing for Science and Engineering with Ian Foster