Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
For decision making, human perception tends to arrange probabilities into above 50% and below – which is plausible. For most probabilistic models in contrast, this… Read More »Setting the Cutoff Criterion for Probabilistic Models
Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities… Read More »Naive Bayes Classifier using Kernel Density Estimation (with example)
Tips Don’t try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the… Read More »Implementing Knowledge Graphs in Enterprises – Some Tips and Trends
Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a… Read More »Six Core Aspects of Semantic AI
This article was written by Lance Whitney. An update to Google’s mobile app and website lets you search for symptoms and receive a list of… Read More »Google's Knowledge Graph Identifies your Medical Symptoms
These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning… Read More »A Standard to build Knowledge Graphs: 12 Facts about SKOS
