Ontotext

FEBRUARY 21, 2024

This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. Natural Language Query (NLQ) has gained immense popularity due to its ability to empower non-technical individuals to extract data insights just by asking questions in plain language.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Ontotext

APRIL 6, 2023

Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. The possibilities are endless!

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

Ontotext

JULY 29, 2021

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprise data? Source: tag.ontotext.com.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Ontotext

AUGUST 31, 2023

While there are many other varying definitions that exist, our definition of the knowledge graph places emphasis on defining the semantic relations between entities, which is central to providing humans and machines with context and means for automated reasoning.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

Ontotext

OCTOBER 28, 2021

It demonstrates how GraphDB and metaphactory work together and how you can employ the platform’s intuitive and out-of-the-box search, visualization and authoring components to empower end users to consume data from your knowledge graph. Although there are already established reference datasets in some domains (e.g.

Visualization 52

Visualization Interactive Knowledge Discovery Dashboards 52

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. References.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136