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Building knowledge graphs to link diverse data, enrich it via text analysis and index it in GraphDB for semantic search.
2020 Sponsor Level:
Gold Sponsor
2019 Sponsor Level:
Gold Sponsor

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May 6th 2020, 11:20pm EST

Vassil Momtchev announces new features of GraphDB and Ontotext platform

May 5th 2020, 1:30pm EST

The enterprise knowledge graphs help modern organizations to preserve the semantic context of abundant accessible information. They become the backbone of enterprise knowledge management and AI technologies with the ability to differentiate things versus strings. Still, beyond the hype of repackaging the semantic web standards for enterprise, few practical tutorials are demonstrating how to build and maintain an enterprise knowledge graph. This tutorial helps you learn how to build an enterprise knowledge graph beyond the RDF database and SPARQL with GraphQL protocol. Overcome critical challenges like exposing simple to use interface for data consumption to users who may be unfamiliar with information schemas. Control information access by implementing robust security. Open the graph for updates, but preserve its consistency and quality. You will pass step by step process to (1) start a knowledge graph from a public RDF dataset, (2) generate GraphQL API to abstract the RDF database, (3) pass a quick GraphQL crash course with examples (4) develop a sample web application. Finally, we will discuss other possible directions like extending the knowledge graph with machine learning components, extend the graph with additional services, add monitoring dashboards, integrate external systems. The tutorial is based on Ontotext GraphDB and Platform products and requires basic RDF and SPARQL knowledge.