The ability for machines to reason—not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge—is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI…

I'm relatively old school, semantically speaking: my first encounters with RDF was in the early 2000s, not long after Tim Berners-Lee's now-famous article in Scientific American introducing the Semantic Web to the world. I remember working through the complexities of RDFS and OWL, spending a long afternoon with one of the editors of the SPARQL…

Cardinality and participation explained for E-R diagrams.

• How to show cardinality/participation for several methodologies.
• Examples of notation and diagrams.

In my previous posts, I discussed some E-R diagram…

In my previous post in which I discussed names, I also led in with the fact that I am a writer. Significantly, I did not really talk much about that particular assertion, because is in fact comes with its own…

• E-R diagrams capture meaning in your data.
• This high-level visual tool identifies core components of an enterprise
• Simple steps to follow to create the diagram.

Entity-Relationship (E-R) Modeling is one approach to visualize what story your data is trying to tell. This goal of this predecessor to…

I wrote on this topic way back in 2016, but when a recent reader indicated that the original article didn't have images anymore (it happens), it seemed like a good opportunity to write about it again.

I am Kurt Cagle, or, according to my birth certificate, Kurt Alan Cagle. My name is Kurt Cagle.

Now, think about that…

SPARQL is a powerful language for working with RDF triples. However, SPARQL can also be difficult to work with, so much so that it often is not utilized anywhere near as often for its advanced capabilities, which include aggregating content, building URIs, and similar uses. This is the second piece in my exploration of OntoText's GraphDB…

Why Question-Answering Engines?

The search only for documents is outdated. Users who have already adopted a question-answering (QA) approach with their personal devices, e.g., those powered by Alexa, Google Assistant, Siri, etc., are also appreciating the advantages of using a "search engine" with the…

For regular readers of the (lately somewhat irregularly published) The Cagle Report, I've finally managed to get my feet underneath me at Data Science Central, and am gearing up with a number of new initiatives, including a video interview program that I'm getting underway as soon as I can get the last of the physical infrastructure (primarily…

Several years ago, the typical company website fit into a predefined template - a home or landing page (usually talking about how innovative the company was), a products page, a business client testimonials page, a blog, and an "about us" page. However, as the number of products or services have multiplied, and as the demands for supporting those services…

The organization of knowledge on the basis of semantic knowledge models is a prerequisite for an efficient knowledge exchange. A well-known counter-example are individual folder systems or mind maps for the organization of files. This approach to knowledge organization only works at the individual level and is not scalable because it…

The still young discipline of the management and governance of knowledge graphs (KG) is gradually beginning to consolidate on…

Data rules the world and, as a result, we want data to drive all the big decisions about how the world works because we believe in rationality, evidence, and science. But most organizations struggle to leverage that data to its competitive advantage. It’s probably even accurate to say that, statistically speaking, almost all organizations fail…

Artificial Intelligence is changing how data management and analysis is handled in the age of big data. Organizations are generating more data than ever before and the process of accessing data using traditional methods is not only time consuming but also poses a threat to the data sets. To handle this challenge, AI changes the way we view data in…

A little soup is quickly cooked. But if many chefs are working together on a larger menu that will eventually be appreciated by a banquet of guests, good preparation is key. In this article we will focus on the preparation phase and outline what it means to introduce…

Network graphs play a large part in both computing and data science, and they are essential for working with (and visualizing) both semantic graphs and property graphs. Nearly thirty years ago, AT&T produced a set of libraries called graphviz which were…

Let’s understand the GAN(Generative Adversarial Network).

Generative Adversarial Networks were invented in 2014 by Ian Goodfellow(author of best Deep learning book in the market)  and his fellow researchers. The…

In this blog, I will introduce a R package for Heterogeneous Ensemble Learning (Classification, Regression) that is fully automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.…

I am reading a very interesting paper called Principles and Practice of Explainable Machine Learning by

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI).

XAI is a complex subject…