Author: Charles Ivie

There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.

In a previous post, we described how to connect Amazon Neptune to metaphactory, securely, and then how to explore and search the Neptune graph data using metaphactory. In this post, we show how you can use metaphactory to build an end user application using its dynamic model driven components, driven by SPARQL queries.

Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. There are many applications and use cases that are enabled by knowledge graphs. Graphs are a natural way to model and represent information about the world. This idea is not new, but has now […]

Semantic reasoning is a powerful form of symbolic AI that brings meaning to data. At a high level, this is achieved by inferring new facts from existing information (or base facts) using a data model and knowledge of the domain. It can be useful for performing calculations, ensuring consistency, and detecting intricate patterns. Semantic reasoning […]

In this post, I describe firstly how you can create a new Amazon Neptune cluster on the console. then by using a NAT Gateway configure your Amazon VPC network to enable SPARQL 1.1 Federated Query.

In this post, I show you how to use SPARQL 1.1 Federated Query in Neptune to get data about soccer teams in the UK from an external dataset, DBpedia (a well-known public dataset of Wikipedia data). Using the DBpedia publicly accessible SPARQL endpoint, I link the data from DBpedia to data that I add to the Neptune cluster.