Remove Document Remove Knowledge Discovery Remove Metadata
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

Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

This is accomplished through tags, annotations, and metadata (TAM). So, there must be a strategy regarding who, what, when, where, why, and how is the organization’s content to be indexed, stored, accessed, delivered, used, and documented. Smart content includes labeled (tagged, annotated) metadata (TAM).

Strategy 267
Insiders

Sign Up for our Newsletter

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

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.

article thumbnail

Knowledge Graphs 101: The Story (and Benefits) Behind the Hype

Ontotext

Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. As a hub for data, metadata, and content, they provide a unified, consistent, and unambiguous view of data scattered across different systems.

article thumbnail

Enrich your serverless data lake with Amazon Bedrock

AWS Big Data

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. End-users often struggle to find relevant information buried within extensive documents housed in data lakes, leading to inefficiencies and missed opportunities.

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

Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. million users.