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

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

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

Ontotext

MARCH 8, 2023

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Graph-based solutions further leverage the relationships among the entities involved to create a semantically enhanced machine learning model.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

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