One of the most significant developments about the current resurgence of statistical Artificial Intelligence is the emphasis it places on knowledge graphs. These repositories have paralleled the contemporary pervasiveness of machine learning for numerous reasons, from their aptitude for preparing training datasets for this technology to pairing it with AI's knowledge base for consummate AI. Consequently, graph technologies are becoming fairly ubiquitous in a broadening array of solutions from Business Intelligence mechanisms to Digital Asset Management platforms. With tools like GraphQL gaining credence across the data landscape as well, it's not surprising many consider knowledge graphs one of the core technologies shaping modern AI deployments. As such, it's imperative to understand that all graphs are not equal; there are different types and functions ascribed to the various graphs vying for one another for the knowledge graph title.
Although there has been great progress in artificial intelligence (AI) over the past few years, many of us remember the AI winter in the 1990s, which resulted from overinflated promises by developers and unnaturally high expectations from end users. Now, industry insiders, such as Facebook head of AI Jerome Pesenti, are predicting that AI will soon hit another wall--this time due to the lack of semantic understanding. "Deep learning and current AI, if you are really honest, has a lot of limitations," said Pesenti. "We are very, very far from human intelligence, and there are some criticisms that are valid: It can propagate human biases, it's not easy to explain, it doesn't have common sense, it's more on the level of pattern matching than robust semantic understanding." Other computer scientists believe that AI is currently facing a "reproducibility crisis" because many complex machine-learning algorithms are a "black box" and cannot be easily reproduced.
Knowledge graphs have been around for almost half a century – as the term was first coined in 1972! For a long time, they simply languished in the academic world until Google announced their knowledge graph in 2012. Since then, knowledge graphs have evolved quite dramatically, and now there is no turning back. The last 10 years have seen a meteoric rise in machine learning (ML) and artificial intelligence (AI). Because of their ability to drive intelligence into data and add context, knowledge graphs are used to make ML and AI more reliable, robust, trustworthy, and explainable.
An inordinate amount of some of the most vital aspects of Artificial Intelligence--from data engineering to data science, data preparation to machine learning--rely on one indispensable prerequisite: data modeling. Without effective data modeling, organizations can't integrate data across sources to build advanced analytics models. Data modeling is foundational to assembling training datasets, utilizing specific data for end user applications, and scaffolding predictive cognitive computing models. Consequently, it behooves companies to make the modeling process as efficient as possible to achieve the following three benefits that optimize their modeling endeavors--and the advanced analytics applications and use cases they support. These advantages are difficult, if not impossible, to realize with traditional relational approaches to data modeling.
The post-big data landscape has been shaped by two emergent, intrinsically related forces: the predominance of cognitive computing and the unveiling of the data fabric architecture. The latter is an overlay atop the assortment of existing distributed computing technologies, tools and approaches that enable them to interact for singular use cases across the enterprise. Gartner describes the data fabric architecture as the means of supporting "frictionless access and sharing of data in a distributed network environment." These decentralized data assets (and respective management systems) are joined by the data fabric architecture. Although this architecture involves any number of competing vendors, graph technology and semantic standards play a pivotal role in its implementation.