Enrich Your Knowledge Graph Experience with GraphDB 8.8

In this webinar, we will demonstrate the new features of GraphDB 8.8, including MongoDB integration for large-scale metadata management, new semantic similarity search, based on graph embedding, and faster and more flexible full-text search (FTS) connectors.

This webinar has been recorded and available on YouTube.

Ontotext has just released GraphDB 8.8.0 which now offers MongoDB integration for large-scale metadata management, new semantic similarity search, based on graph embedding.

One of the most exciting features of GraphDB 8.8 is the new plugin for MongoDB. It enables users to query from MongoDB of JSON-LD serialized data. The MongoDB integration also allows developers and solution architects to substantially improve the write performance of their RDF solutions when dealing with document-centric data. 

In our previous release, we introduced a new plugin returning similar terms, documents and entities, which added support for concept-matching in knowledge graphs. GraphDB 8.8 now allows users to perform semantic similarity searches based on embedding of relationships in a graph (Subject-Predicate-Object triples) in a highly-scalable vector space model. 

In this webinar you will learn how to:

* Query MongoDB with SPARQL;

* Create text similarity index to perform similarity searches based on the text in your Knowledge Graph;

* Create graph similarity index based on the Subject-Predicate-Object triples in GraphDB and find similar entities with SPARQL.

Who is this webinar for:

* Technical people confident in SPARQL;

* Consultants and Solution Architects;

* Linked Data Scientists.

Expected duration: 45 min

Meet the speakers

Desislava Hristova

Desislava Hristova

Senior Software Developer, GraphDB DevEx

Desislava Hristova has a BS in Computer Science from Sofia University. In the recent years, she is developing mainly the GraphDB Workbench both back and front-ends. She is currently leading the visual data representation extensions. Before that, she was part of the Web Mining team developing HTML specific annotation tool.

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