Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies,Science, Technology and Medicine Publishers, etc.
Text Analysis is about parsing texts in order to extract machine-readable facts from them. The purpose of Text Analysis is to create sets of structured data out of heaps of unstructured, heterogeneous documents.
Metadata represents data about data and enriches the data with information that makes it easier to find, use and manage. Semantic metadata helps computers to interpret data by adding references to concepts in a knowledge graph.
GraphDB Fundamentals builds the basis for working with graph databases that utilize the W3C standards and particularly GraphDB. It is a training class delivered in a series of ten videos that will accompany you in your first steps of using triplestore graph databases.
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.
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.