Blog

Ontotext’s Relation and Event Detector (RED) is a tool that analyzes textual content to identify entities, trends and events. Watch the following videos to learn more about how the RED works. The introductory video outlines RED’s capabilities for analyzing textual data and extracting meaningful information about companies, events, and entities. RED uses a knowledge graph […]

In our interconnected world, information travels across the globe at unprecedented speed and disinformation is getting spread even faster and reused across geographies. To help in the battle against disinformation, Ontotext is tackling the challenge of identifying narratives or disinformation campaigns. This covers a range of tasks starting from analysis of textual content in multiple

A risk and opportunity event refers to an occurrence that may positively or negatively impact the stock market performance of a company or industry sector. The ability to detect and analyze such events is crucial. Ontotext’s Relation and Event Detector (RED) is designed to assess and analyze the impact of market-moving events. RED answers key

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. Most frequently, it uses a vector database indexed with proprietary content and available for a retrieval component to query it. Let’s see how we can achieve our own RAG using

Entity linking is the process of automatically linking entity mentions from text to the corresponding entries in a knowledge base. It has been an important capability for Ontotext ever since we dove into Natural Language Processing (NLP), as it is a crucial aspect of the interplay between text analysis and knowledge graphs. Now comes Ontotext’s

Ontotext is pleased to announce the new version of the Ontotext Metadata Studio (OMDS). It now enables you to tag your content with CEEL – our new generation class-leading text analytics service performing Common English Entity Linking. This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the

Natural Language Querying (NLQ) refers to the process of querying a database or an information system using natural language, such as English, instead of formal query languages, such as Structured Query Language (SQL). NLQ allows users to interact with databases or systems more intuitively and user-friendly. They can simply type or speak their queries, resembling

INTRODUCTION InnoGraph will build a holistic knowledge graph of innovation based on Artificial Intelligence (AI), and more generally of the global “hitech” ecosystem. It is a key use case of the enrichMyData project. InnoGraph originated from a partnership between OECD and the Jožef Stefan Institute (JSI) on the OECD.AI Policy Observatory and prior Ontotext experience

Could you give a quick outline of your history and the skills you bring to the enrichMyData project? I have been a data engineer at Ontotext for the last 7 years and have been part of countless data integration projects, both small and relatively large in scale. Ontotext is a unique company as we are