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.
The classic one-line definition of Knowledge Management was offered up by Tom Davenport early on: 'Knowledge Management is the process of capturing, distributing, and effectively using knowledge.' Probably no better or more succinct single-line definition has appeared since.
Imagine you could get the entire web in a database, and structure it. Then you would be able to get answers to complex questions in seconds by querying, rather than searching. This is what Diffbot promises.
If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.
What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.
Knowledge Graphs have a real potential to become highly valuable, topical and relevant. If only we can get them prised out of the engineer, data scientists, or software experts hands.