Ontotext

DECEMBER 1, 2023

It enriched their understanding of the full spectrum of knowledge graph business applications and the technology partner ecosystem needed to turn data into a competitive advantage. Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises.

Metadata 78

Metadata Sales Consulting Enterprise 78

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the adoption of knowledge graphs. Graph solutions have gained momentum due to their wide-ranging applications across multiple industries. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

SEPTEMBER 15, 2020

It must not attempt to force or predict how the data is to be used but to enable that decision to be made by today’s user (and tomorrow’s) while still maintaining organization appropriate access control and security. Consider using data catalogs for this purpose. Clean data to ensure data quality.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

FEBRUARY 19, 2020

We hope it will bring some clarity to the topic and will help you get a better understanding of what it takes to craft a knowledge graph the semantic data modeling way. Ontotext’s 10 Steps of Crafting a Knowledge Graph With Semantic Data Modeling. Clean your data to ensure data quality.

Modeling 90

Modeling Knowledge Discovery Data Quality Visualization 90

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?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

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