Businesses have to face the pace of constantly evolving data needs. Knowledge graphs might be the solution. Venture Beat brought this interesting topic to us in their article, “Why knowledge graphs are key to working with data efficiently, powerfully.”
As many as 50% of Gartner client inquiries on the topic of artificial intelligence (AI) involve a discussion involving the use of graph technology. Knowledge graphs can help companies move away from traditional databases by using the power of natural language processing, machine learning and semantics.
Every large enterprise wants to exploit available data to bring more insights for doing business at scale. To achieve this, connected data is seen as the solution.
The knowledge graph represents a collection of interlinked descriptions of entities – objects, events or concepts. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.
Bringing knowledge graphs and machine learning together has great potential to improve the accuracy of the systems and extend the range of machine learning capabilities.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
