Rocket-Powered Data Science

JULY 6, 2021

Techniques that both enable (contribute to) and benefit from smart content are content discovery, machine learning, knowledge graphs, semantic linked data, semantic data integration, knowledge discovery, and knowledge management. Collect, curate, and catalog (i.e.,

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

Ontotext

DECEMBER 1, 2023

Seen through the three days of Ontotext’s Knowledge Graph Forum (KGF) this year, complexity was not only empowering but key to the growth of knowledge and innovation. Sumit started his talk by laying out the problems in today’s data landscapes. Abstract art and knowledge graphs: embracing your mess!

Metadata 78

Metadata Sales Consulting Enterprise 78

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.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

Ontotext

SEPTEMBER 15, 2020

This multiplicity of data leads to the growth silos, which in turns increases the cost of integration. The purpose of weaving a Data Fabric is to remove the friction and cost from accessing and sharing data in the distributed ICT environment that is the norm. Create a human AND machine-meaningful data model.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

JULY 29, 2021

The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124