In the last year, graph embeddings have become increasingly important in Enterprise Knowledge Graph (EKG) strategy. Graph embeddings will soon become the de facto way to quickly find similar items in large billion-vertex EKGs. And as we have discussed in our prior articles, real-time similarity calculations are critical to many areas such as recommendation, next best action, and cohort building. The goal of this article is to give you an intuitive feeling for what graph embeddings are and how they are used so you can decide if these are right for your EKG project. For those of you with a bit of data science background, we will also touch a bit on how they are calculated. For the most part, we will be using storytelling and metaphors to explain these concepts.

Imagine you're having friends over for lunch and plan to order a pepperoni pizza. You recall Amy mentioning that Susie had stopped eating meat. You try calling Susie, but when she doesn't pick up, you decide to play it safe and just order a margherita pizza instead. People take for granted the ability to deal with situations like these on a regular basis. In reality, in accomplishing these feats, humans are relying on not one but a powerful set of universal abilities known as common sense.

Imagine you're having friends over for lunch and plan to order a pepperoni pizza. You recall Amy mentioning that Susie had stopped eating meat. You try calling Susie, but when she doesn't pick up, you decide to play it safe and just order a margherita pizza instead. People take for granted the ability to deal with situations like these on a regular basis. In reality, in accomplishing these feats, humans are relying on not one but a powerful set of universal abilities known as common sense. As an artificial intelligence researcher, my work is part of a broad effort to give computers a semblance of common sense.

Imagine you're having friends over for lunch and plan to order a pepperoni pizza. You recall Amy mentioning that Susie had stopped eating meat. You try calling Susie, but when she doesn't pick up, you decide to play it safe and just order a margherita pizza instead. People take for granted the ability to deal with situations like these on a regular basis. In reality, in accomplishing these feats, humans are relying on not one but a powerful set of universal abilities known as common sense. As an artificial intelligence researcher, my work is part of a broad effort to give computers a semblance of common sense.

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A girl living with a group of monkeys in a wildlife sanctuary was found by police near the Nepalese border in India. Now the girl -- who didn't appear to understand any language and was walking on all fours -- has been dubbed a "real life Mowgli" after the feral child from Rudyard Kipling's "Jungle Book." Officers found the girl with a pack of wild monkeys two months ago, while performing a routine patrol in the Katarniaghat Wildlife Sanctuary near the Nepalese border, authorities told the Times of India. When officers tried to communicate with the girl, she and the monkeys both screeched at the approaching humans. Authorities were eventually able to convince her to come with them, and she has been recuperating in a local hospital ever since.

Super-statistician Nate Silver has a rival - and it's not human. An Indian artificial intelligence system which correctly predicted the primaries as well as the last three U.S. presidential elections has forecast a win for Donald Trump. MoglA scans and analyzes internet information from sites including Google, Facebook, Twitter and YouTube to make its predictions, CNBC reported. The technology is named after Mowgli, Rudyard Kipling's character in The Jungle Book, because it learns from its environment. Part of the technology's calculations is the amount of engagement a candidate gets online from the general population.