The Extensibility of Knowledge Graphs for Natural Language Understanding

The universal applicability of enterprise knowledge--across use cases, domains, and languages--is widely understood. And, it's likely the main reason adoption rates for knowledge graphs have steadily inclined of late, making them one of the most utilitarian forms of AI available today. True knowledge graphs are extensible and predicated on standards designed to share data of any type. Such graphs are inherently composable, enabling users to either combine them or enrich them with knowledge of all sorts. These options are critical for not only simplifying the management of enterprise knowledge for Natural Language Understanding deployments, but also for redoubling the value organizations reap from knowledge graphs across a burgeoning array of use cases.