What is a Knowledge Graph?
A knowledge graph is a collection of linked entities (objects, events, concepts) that acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.
Why it matters
Graph technologies enable various use cases including recommendation engines, fraud detection systems, identity and access management, and search functionality. Knowledge graphs are integral to many AI applications — Google employs them to enhance search results with informational boxes. These structures also find widespread adoption in e-commerce, healthcare, and financial services sectors, and they are the connective tissue that makes contextual, enterprise-grade AI possible.
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Related concepts
The Graph Intelligence platform enables data analysts to identify trends, detect outliers, and uncover connections within extensive datasets. It provides elastic performance at scale across four key capabilities: Graph Queries for contextual search, Graph Analytics for path finding and community detection, Graph Mining for pattern discovery, and Graph AI for prediction.
Contextual Intelligence is a form of AI which leverages a business context graph to understand the business intent of analytics efforts. Contextual Intelligence applies the business metrics and business logic of a use case to uncover areas of improvement and propose specific actions, with a much higher degree of relevance and personalization to an end user. It generates a recommendation to improve performance against a specific metric and identifies the impact of proposed actions on the metric in order to predict the butterfly effect associated with a potential action.
Composite AI, also known as Multidisciplinary AI, represents the combined application of different AI techniques to improve learning efficiency and broaden knowledge representations. Rather than treating machine learning as a universal solution, organizations combine various AI approaches for better results when addressing complex business challenges. A composite AI system should be built on a "composite architecture" that integrates multiple methodologies: traditional rules-based systems, natural language processing (NLP), optimization techniques, and graph techniques.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI employs sophisticated analysis and logic-based techniques—including machine learning—to interpret events, facilitate and automate decisions, and execute actions. This technology enables machines to understand, respond to, and learn with human-comparable levels of intelligence.