Ontotext

MARCH 13, 2024

Eventually, this led to the transformation of the project into forming an expansive knowledge graph containing all the marketing knowledge we’ve generated, ultimately benefiting the whole organization. OTKG models information about Ontotext, combined with content produced by different teams inside the organization.

Marketing 72

Marketing Knowledge Discovery Metadata Data-driven 72

Rocket-Powered Data Science

JULY 6, 2021

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 266

Strategy Machine Learning Metadata Knowledge Discovery 266

Ontotext

AUGUST 19, 2020

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.

Metadata 59

Metadata Knowledge Discovery Cost-Benefit Data Governance 59

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. With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. 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.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Ontotext

FEBRUARY 21, 2024

RAG and Ontotext offerings: a perfect synergy RAG is an approach for enhancing an existing large language model (LLM) with external information provided as part of the input prompt, or grounding context. So we can easily integrate the information from both textual documents and structured RDF entities into an LLM-driven application.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64