Making Sense of Text and Data
Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Interlink your organization’s data and content by using knowledge graph powered natural language processing with our Content Management solutions.
Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Quick and easy discovery in clinical trials, medical coding of patients’ records, advanced drug safety analytics, knowledge graph powered drug discovery, regulatory intelligence and many more
Make better sense of enterprise data and assets for competitive investment market intelligence, efficient connected inventory management, enhanced regulatory compliance and more
Connect and model industry systems and processes for deeper data-driven insights in:
- Manufacturing
- Automotive Industry
- Energy
- Building Automation
- Infrastructure
- Aerospace & Defense
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
Unlock the potential for new intelligent public services and applications for Government, Defence Intelligence, etc.
Connect and improve the insights from your customer, product, delivery, and location data. Gain a deeper understanding of the relationships between products and your consumers’ intent.
Link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.
Organize your information and documents into enterprise knowledge graphs and make your data management and analytics work in synergy.
Integrate and evaluate any text analysis service on the market against your own ground truth data in a user friendly way.
Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph.
KGF 2021 Talks: Practical Ontology Development and Mapping the Legal Entity Dataset
This talk was presented byPete Rivett, Director of Enterprise Knowledge Graph Foundation, at the Ontotext and Partners Knowledge Graph Forum in October 2021.
Pete Rivett, Director of Enterprise Knowledge Graph Foundation, takes you through a case study on taking a specific large XML dataset and developing a focused ontology, and data transformations, for a set of knowledge graph/linked data use cases. The dataset is the Legal Entity data managed by the Global Legal Entity Identifier Foundation (GLEIF) and the linked data is now publicly available.
The focus is on applying a semantic approach while making the representation understandable, scalable, and integratable with other ontologies (such as the Financial Industry Business Ontology).