Artificial Intelligence (AI) is poised to drive the next wave of technological disruption across nearly every industry. Just like previous technology revolutions in web and mobile, however, there will be winners and losers based on who harnesses this technology for a true competitive advantage.
Neo4j customers are demonstrating that graph database technology brings tremendous value to AI and machine learning projects, especially in the area of knowledge graphs.
Because of their structure, knowledge graphs add essential context for AI applications by capturing facts related to people, processes, applications, data and things, and the relationships among them. They also capture evidence that attributes the strengths of these relationships.
Ready to see how graphs add context to your AI and machine learning applications?
Ernst & Young (EY): Knowledge Graphs, the Path to Enterprise AI
Knowledge graphs are a foundation of artificial intelligence. This presentation includes a range of how-to information for building an enterprise knowledge graph, including how to recognize graph problems.Watch video
Neo4j & Expero: Lower Risk & Stop Fraud Using Graph-Enhanced Machine Learning & AI
Understand how successful financial services, banks and retailers are using graph technology and embedding intelligence to quickly identify risk and fraud patterns as they evolve.Watch video
eBay’s ShopBot Delivers Recommendations with Artificial Intelligence & Neo4j
eBay’s Chief Product Officer explains how he turned to graph technology because existing product searches and recommendation engines were unable to provide contextual information within a shopping request.Read more
Whether your AI solution is chatting with customers or driving an autonomous vehicle, real-time feedback loops are essential for meaningful machine learning that evolves your intelligence models.
Evolves with business requirements
In the emergent industry of AI, business and user requirements are still being defined, tweaked or completely upended. The graph data model is more agile and flexible than a traditional RDBMS in meeting these new and changing requirements.
Disparate (and changing) data sources
Your machine learning algorithms consume data from a variety of ever-changing sources and data types, meaning you need a database with a versatile and adaptable schema.
Multiple hop queries
In order to determine context for the most appropriate action, AI solutions must query several layers deeper within their databases than previous technologies required. This means the database layer must be able to support multi-hop (4+) queries without affecting performance.
Native graph store
Unlike relational databases, Neo4j stores interconnected data that is neither linear nor purely hierarchical. Neo4j’s native graph storage makes it easier to decipher your data by not forcing intermediate indexing at every turn.
Neo4j’s versatile property graph model makes it easier for organizations to evolve machine learning and artificial intelligence models – especially for those based on knowledge graphs.
Performance and scalability
Neo4j’s native graph processing engine supports high-performance graph queries on large datasets to enable real-time decision making by AI solutions and the engineers behind them.