Ontotext

AUGUST 16, 2019

Let’s start with a quick definition of the basics. Semantic technology is a broad technological term that covers specific technological approaches, principles and methodologies for managing data and knowledge. Some of that journey has been recorded in a previous blog post. What This Training Is. Want to see for yourselves?

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

Ontotext

FEBRUARY 19, 2020

Today, as the number of decision-makers recognizing the importance of more dynamic, contextually aware and intelligent information architectures is growing, so is the number of companies with solutions based on knowledge graphs. Yet, the concept of knowledge graphs still lives without an agreed-upon description or shared understanding.

Modeling 90

Modeling Knowledge Discovery Data Quality Technology 90

Ontotext

APRIL 4, 2019

We apply Artificial Intelligence techniques to understand the value locked in this data so we can extract knowledge that can benefit people. Milena Yankova : There are two definitions of Artificial Intelligence. One area where computers will definitely not replace us is our creativity. What exactly do you do for them?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. anomaly detection).

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. To understand this better we need a few definitions. The paper gained much attention because, having conducted the largest study of its kind, it was understood to debunk the idea definitively.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Again, the overall aim is to extract knowledge from data and, through algorithms based on artificial intelligence, to assist medical professionals in routine diagnostics processes.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40

Ontotext

AUGUST 31, 2023

The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Rare binary event example In the previous post , we discussed how rare binary events can be fundamental to the LSOS business model. Another way to build a classifier for variance reduction is to address the rare event problem directly — what if we could predict a subset of instances in which the event of interest will definitely not occur?

Experimentation 40

Experimentation Metrics Statistics Measurement 40