Knowledge graphs represent 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. This interesting topic came to us from Business 2 Community in their article, “How Knowledge Graphs Impact Search Intent.”

As the drive for digital transformation becomes an imperative for companies seeking to compete and succeed in all industry sectors, intelligent tools and services are being leveraged to enable speed, insight and accuracy.

At the heart of search lies knowledge graphs. One misconception is that every knowledge base is a knowledge graph. A key feature of a knowledge graph is that entity descriptions should be interlinked to one another. The definition of one entity includes another entity. Knowledge bases without formal structure and semantics also do not represent a knowledge graph.

Changing search to found is important to knowledge management, and to Access Innovations, one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.

Melody K. Smith

Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.