Ontotext Demo Day

GraphDB 10 debut: almost two and a half years after its last major release, GraphDB marks the next significant milestone in its development!

Expect to see new features and major improvements in deployment and migration, in the Connectors, in license handling and the GraphDB 10 crown jewel – cluster management based on Raft consensus algorithm, quorum-based voting and leader election.

Suitable for: Current & Future Users of GraphDB

A quick start knowledge graph scenario for Financial Services. Get and link external data about companies, extract signals from text, virtually access stock prices and find correlations to the signals by leveraging FIBO (Financial Industry Business Ontology), GLEI (Global Legal Entities) dataset and LCC (Languages, Countries and Currencies) reference data.

Suitable for: Information & Solution Architects

An introduction to Ontotext Metadata Studio (OMDS) – a new Ontotext product that helps organizations find out if they can automatically extract the knowledge locked within their valuable unstructured content.

Go on a walkthrough of some key OMDS features that enable:

• semantic representation of any use case within the product;
• establishing the Ground Truth for a set of documents;
• evaluating any text analysis service against the Ground Truth baseline; and
• analyzing the results & unlocking insights.

Suitable for: NLP Engineers, Data Scientists, Non-Technical SME, Knowledge Workers, Business Analysts

A demonstration of a data cleaning, reconciliation and RDF-ization workflow with OntoRefine aiming to enhance an existing knowledge graph with new information contained in tabular data.

Watch us clean up a tabular dataset with OntoRefine, set up a project-specific reconciliation service on top of an existing knowledge graph and use it from the OntoRefine project to match strings in the source data to entities in the knowledge graph. The enriched data will then be converted to RDF, using the Visual RDF Mapper, and imported into the existing knowledge graph in the GraphDB repository.

Suitable for: GraphDB Users, ETL Enthusiasts, Graph Data Scientists

An introduction to Tag Essentials – the first of a series of Content Management offerings blending two of Ontotext’s strongest areas of expertise: knowledge representation and text analysis.

Go on a walkthrough of some key Tag Essentials features such as easy use of tagging functionality from scratch, making use of existing third party tagging functionality and evaluating the quality of that tagging according to your guidelines and standards. The entry-level, out-of-the-box functionality enables the bootstrapping of a text analysis offering without relying on the complex, and usually expensive, prerequisites for creating such offerings.

Suitable for: Non-Technical SMEs & Taxonomists (who want to bootstrap tagging functionality against their own taxonomy from scratch)

A case for using the virtualization approach when incorporating transactional data. Find out how virtualization helps incorporate transactional records such as price points and use them in combination with the “native” graph data.

Although graphs are the best place for your semantic model, reference data, annotations and entity records, they are not the most effective structure to store such data. Learn how semantic mapping and transformation are a great introduction to the added value provided by the graph modeling on top of “traditional” relational data.

Suitable for: Information & Solution Architects, Developers (who consider a switch to graph technologies)

A presentation of Ontotext’s Target Discovery solution, which helps biotech and Pharma companies identify new drug targets and drug repurposing candidates quickly and reliably in a variety of therapeutic areas.

Learn how you can unlock relevant information and insights in both structured and unstructured reference data sources, mapped and managed in a central knowledge graph. This makes it easy to extract information about any entity and context of interest in seconds. It’s also easy to apply customizable algorithmic and visual analytics on top of the data model for in-depth insights and critical decision support.

Suitable for: Pharma & BioTech Researchers, Data Analysts, Scientists

A presentation of the recently started work on the Transparency Energy Knowledge Graph (TEKG) project, which aims to convert the ENTSO-E electricity market transparency data to a semantic knowledge graph and complement it with external data sources.

Tune in to learn how we have implemented a number of advanced validations over fundamental electricity data such as Energy Identification Code files, power plant data and specific market data. Find out how knowledge graphs can help ENTSO-E and national electricity authorities (Transmission System Operators, TSO) diagnose data quality problems and improve data collection procedures and legislation.

Suitable for: Data Scientists, Network Engineers, Data Architects & Analysts