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.
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 search bridges the language gap between humans and machines, and takes us further on our quest for meaningful information and knowledge discovery.
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.
Information extraction is the process of extracting specific information from textual sources. It enables the automation of tasks such as smart content classification, integrated search, management and delivery. It also facilitates data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.