Why Knowledge Graphs Are Foundational to Artificial Intelligence

AI is poised to drive the next wave of technological disruption across industries. Like previous technology revolutions in Web and mobile, however, there will be huge dividends for those organizations who can harness this technology for competitive advantage.

I spend a lot of time working with customers, many of whom are investing significant time and effort  in building AI applications for this very reason. From the outside, these applications couldn’t be more diverse – fraud detection, retail recommendation engines, knowledge sharing – but I see a sweeping opportunity across the board: context.

Without context (who the user is, what they are searching for, what similar users have searched for in the past, and how all these connections play together) these AI applications may never reach their full potential. Context is data, and as a data geek, that is profoundly exciting.

We’re now looking at things, not strings

The best example of the value of context within data is the consumer Web. For example, most of us interact with Google every day. To understand the value of context in AI better, let’s look back to 2012, when Google fundamentally transformed search from “strings” to “things.”

Google is a clear leader in the field of AI through its own innovation and strategic acquisition, not least of which was its acquisition of AI firm DeepMind in 2014. But a smaller, lesser publicized move, I believe, deserves more attention: In 2012, Google introduced its “Knowledge Graph.”

Before the Knowledge Graph, searching via Google was a “string.” If I wanted to know when “Star Wars: The Last Jedi” was playing, I had to search for a movie theatre, then click through to find the right movie and then find and scroll through the showtimes. I had to hop across multiple links.

Today, if I type in “Star Wars: The Last Jedi” not only do I get a link to that movie’s page, but Google’s Knowledge Graph also knows what action I most likely want to take based on previous searches. Thanks to the Knowledge Graph, I am also offered multiple options like showtimes in my area, a way to buy tickets and the movie’s Rotten Tomatoes score.

Per Wikipedia, Google’s Knowledge Graph “uses a graph database to provide structured and detailed information about the topic in addition to a list of links to other sites.” This knowledge graph, built on top of a graph database, has allowed Google to focus its search on things – or concepts – and understand exactly what you’re looking for based on context.

Knowledge graphs are a means of storing and using data, which allows people and machines to better tap into the connections in their datasets. By contrast typical NOSQL pattern is simple  “store and retrieve.”

You store data and then you symmetrically retrieve it. That method does very little for the user in terms of context and connections. With knowledge graphs, every time you enter data you enrich the entire data ecosystem, because it’s connected to everything else. The more data, the more context. And this contextual value grows exponentially like Metcalfe’s law of the network, because networks are graphs.

Because of their structure, knowledge graphs capture facts related to people, processes, applications, data and things, and the relationships among them. They also capture evidence that can be used to attribute the strengths of these relationships. This is where the context is derived from.

An important question: what separates knowledge graphs from data lakes or data warehouses? The answer is operational convenience. Data warehousing is good for static BI projects, but knowledge graphs must be able to power insights in real time, otherwise they simply won’t work for applications like real-time recommendations, fraud detection or knowledge sharing.