Protecting drivers, helping increase diversity, slowing the effects of aging and more, artificial intelligence (AI) and its ability to process and find patterns in mass amounts of data has been crucial in helping us face some of the world’s most intricate problems.

AI is far from true intelligence though. Though it can recognize patterns and make recommendations, and even recreate language that mimics that of a real human, the power of AI is limited by its inability to understand context.

Except when aided by knowledge graphs.

What Is a Knowledge Graph?

A database that provides structured and connected information, knowledge graphs help machine learning build contextual results. Beyond just data storage, knowledge graphs capture the relationships between various data points.

Knowledge graphs, initially constructed and maintained by hand, are not a new technology. Google also uses knowledge graphs for its most popular search terms, drawing from contextual factoids to provide the box next to search main search results that shows a set of facts about the search term, such as a celebrity, movie or company.

In online shopping experiences, knowledge graphs parse search words to map the data to inventory — not simply locate items related to “brown” and “bag,” but “brown bag,” for example — enabling AI to offer accurate recommendations for users.

A Trusted Partner

With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns. This allows AI to be a more trustworthy partner as we search the web.

One company, Diffbot, is expanding the power of knowledge graphs to everything on the web by automating the knowledge graph construction process. Its AI browses and stores information from the web at high speeds, adding continuously updated factoids to its knowledge graph. The solution helps customers redefine their business operations, find new customers and partners, and understand clients.

Enterprise knowledge graphs are able to help retail stores locate counterfeits, more accurately predict client interest and intent, easily gather insights to understand patterns in data, and more, empowering artificial intelligence to more intelligently solve problems.