A knowledge graph, which can be considered a type of ontology, depicts “knowledge in terms of entities and their relationships,” according to GitHub. An example of a knowledge graph is shown below. Knowledge graphs developed from the need to do something with or act upon information based on context. For example, knowledge graphs help identify fraud, keep track of inventories, and write novels. Knowledge graphs have been gaining more traction with machine learning so that AI processes can use the same information, as needed, for multiple situations. Knowledge graphs simplify complex concepts at one glance and promise good training data for AI to learn new tasks.

Key abilities of knowledge graphs, according to Ralph Hodgson, CTO of TopQuadrant, include:

• Extensibility: The ability to accommodate diverse data and metadata that evolve over time.
• Introspection/Query Ability: Models that can be inspected to find what things are knowable and findable.
• Semantic: The meaning of the data is stored within the graph alongside the data to understand connections.
• Intelligence Enabling: The ability to infer dependencies and other relationships between objects.

Other Definitions of Knowledge Graphs Include:

• “An interconnected set of information, able to meaningfully bridge enterprise data silos and provide a holistic view of the organization through relationships.”(Amber Lee Dennis)
• Layers atop the existing data infrastructure that “reveal the relationships within the data, regardless of the source or format.” (Keith D. Foote)
• An “enhanced graph database enriched with business rules that allow for inference to be performed upon the connected data.” (Keith D. Foote)
• A “means of storing and using data, which allows people and machines to better tap into the connections in their datasets.” (Datanami)
• A “database which stores information in a graphical format – and, importantly, can be used to generate a graphical representation of the relationships between any of its data points.” (Forbes)
• “Encyclopedias of the Semantic World.” (Forbes)

Knowledge Graph Use Cases Include:

• Standardizing health vocabularies and taxonomies to code medical bills consistently.
• Making all of Noam Chomsky’s published works easily available and searchable in the context of topics and concepts.
• KBpedia, a knowledge map, combines key aspects of Wikipedia, Wikidata, schema.org, DBpedia, GeoNames, OpenCyc, and UMBEL into an integrated whole that is configurable for computational knowledge graphs.
• Asking eBay to show brown leather Coach messenger bags under $100. A knowledge graph then helps figure out the best follow-up questions to ask in order to find the best results in the least amount of time.
• Tracking networks of people and the connections between them on Facebook.

Businesses Use Knowledge Graphs to:

• Capture all enterprise data assets across a full range of contexts, both technical and business
• Harmonize data according to standard data models
• Standardize data classifications
• Facilitate AI and machine learning
• Unify data and show relationships

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