article thumbnail

Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

This is accomplished through tags, annotations, and metadata (TAM). Smart content includes labeled (tagged, annotated) metadata (TAM). The key to success is to start enhancing and augmenting content management systems (CMS) with additional features: semantic content and context. Collect, curate, and catalog (i.e.,

Strategy 267
article thumbnail

Three’s Company Too: Metadata, Data and Text Analysis

Ontotext

Metadata used to be a secret shared between system programmers and the data. Metadata described the data in terms of cardinality, data types such as strings vs integers, and primary or foreign key relationships. Inevitably, the information that could and needed to be expressed by metadata increased in complexity.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Ontotext Marketing Gets a Boost from Knowledge Graph Powered LLMs

Ontotext

We store this in GraphDB by leveraging standard tooling for knowledge graph management. Through Ontotext Metadata Studio (OMDS), we then apply semantic content enrichment using text analysis based on our marketing vocabularies. In this way, we benefit from better SEO and semantic-driven content discovery.

article thumbnail

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

Knowledge graphs (KG) came later, but quickly became a powerful driver for adoption of Semantic Web standards and all species of semantic technology implementing them. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.

article thumbnail

On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

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.

article thumbnail

From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

This means the creation of reusable data services, machine-readable semantic metadata and APIs that ensure the integration and orchestration of data across the organization and with third-party external data. Knowledge Graphs are the Warp and Weft of a Data Fabric. This provides a solid foundation for efficient data integration.

article thumbnail

Why Establishing Data Context is the Key to Creating Competitive Advantage

Ontotext

Without metadata management and other data-related operations with semantic technologies, organizations often struggle to connect data sets and achieve a unified view of their enterprise data. Enter the Semantic Edge Era: How to Derive Value from Semantic Metadata The problem with Big Data is not the data itself.