Applications of Knowledge Graphs

Mike Tung on lessons learned from building, maintaining, and using a knowledge graph with over a trillion connected facts.

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Mike Tung is founder and CEO of Diffbot, a startup that crawls the web and provides one of the most comprehensive knowledge graphs, accessible through a variety of simple interfaces. Although knowledge graphs can be described in a variety of ways, an article from 2016 suggested the following succinct framing:

    A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.

We discussed applications of knowledge graphs in search, recommendations, as features in AI and machine learning applications, and as core components in AI systems capable of blending ML models with knowledge.

Mike Tung:

Google really first popularized that word with the Google Knowledge Graph. This was basically a startup they acquired called Metaweb which had this knowledge graph called Freebase. When you search for something on Google, on the right hand side, you see these knowledge panels. So that’s what people refer to knowledge graphs in search – they’re referring to this information in a knowledge panel.

Many major companies now have knowledge graphs, Spotify, Netflix, Uber all have Knowledge Graph teams. When they are doing search and recommendations within their apps, like when you open up the Uber Eats app, for example, it’s recommending dishes and restaurants for you. In this example Uber is pulling that from a backend knowledge graph where they have modeled out all the dishes and all the restaurants and in specific locations. Using that information when doing ranking is very natural and produces better and also more explainable recommendations.

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[Image: Knowledge Graph of the public web from Diffbot]