SPARQL enables users to query information from databases or any data source that can be mapped to RDF. The SPARQL standard is designed and endorsed by the W3C and helps users and developers focus on what they would like to know instead of how a database is organized.
How to choose the best data management solution? Here is a quick comparison between some of the major players in the game: Relational databases, Property graphs and RDF databases.
The RDF triplestore is a type of graph database that stores semantic facts in RDF. It stores data as a network of objects with materialized links between them. Being very flexible and cost-effective, it is the preferred choice for managing highly interconnected data.
Semantic annotation is the process of attaching additional information to various concepts (e.g., people, things, places, organizations, etc.) in a given text or any other content. Unlike classic text annotations, which are for the reader’s reference, semantic annotations are used by machines.
Semantic data integration is the process of combining data from disparate sources and consolidating it into meaningful and valuable information through the use of Semantic Technology. With Ontotext’s semantic integration tools, users can quickly design data processing jobs and integrate massive amounts of data.
RDF stands for Resource Description Framework and is a standard for data interchange, developed and agreed upon by W3C. While there are many conventional tools for dealing with data and more specifically for dealing with the relationships between data, RDF is the easiest, most powerful standard designed by now.
The NoSQL graph database is a technology for data management designed to handle very large sets of structured, semi-structured or unstructured data. It helps organizations access, integrate and analyze data from various sources, thus helping them with their big data and social media analytics.
Machine learning (ML) is about teaching computer programs to recognize images, words and sounds by giving them a lot of examples to learn from. Once developed and trained, such algorithms help create systems that can automatically respond to and interpret data.