Building and maintaining accessible, accurate and consistent data within an organisation is very challenging. These difficulties are further compounded where data is shared with partners or imported from external sources.
The principles underpinning Enterprise Knowledge Graphs aim to build and standardise the terms (Vocabularies) used within data sets, document the structure of the underlying information in machine readable formats (Ontologies) enabling structured (Semantic) search, automation of business logic (Reasoning), portable information exchange (Linked data) and a solid foundation for machine learning.
Knowledge Graphs are built using W3C standards. RDF underpins the initiative to build a Web of data. Its firm foundation on XML Schema Datatypes has been instrumental in the standardisation of data formats for storage and exchange.
RDF Schema and the Web Ontology Language (OWL 2) define standards for capturing the structured of knowledge such that inferences can be drawn (entailed) from the knowledge, a process known as reasoning.
There are many resources that discuss in greater depth the advantages of these technologies but these are elegantly summarised in Gartner's most recent "100 Data and Analytics Predictions Through 2024":
“By 2025, SaaS-based knowledge graphs will boost workforce digital dexterity by delivering personalized content recommendations, insight into work patterns, and individual skills development guidance.”
The creation of structured data with standardised vocabularies has already had significant impact in the real world:
Many large organisations are running initiatives to implement these technologies (see Knowledge Graph Survey). However, any sized business can benefit from organising their knowledge in a human and machine readable form.