A Neo4j knowledge graph is an insight layer of interconnected data enriched with semantics, so you can reason with the underlying data and use it confidently for complex decision-making.
Building Knowledge Graphs: A Practitioner's Guide
A knowledge graph gets richer as new data is added. Through a combination of data, graph, and semantics (meaning), you get a knowledge graph with deep, dynamic context.
Bridge together diverse and disparate data silos regardless of data type, such as structured, unstructured, and semi-structured.
Map data and draw connections among them for the first layer of dynamic context, which provides immediate understanding.
Apply semantics to provide deeper context to connected data. The deeper the context, the more powerful the insights.
Only graphs excel at managing connected data and complex queries, because relationships are at the core of the data model. Knowledge graphs add an additional layer of context to deepen the connections.
Connect and contextualize the variety of structures and formats of your data so you can operate more efficiently and effectively.
Gain complete visibility into data, processes, products, customers, and ecosystems for increased efficiency and enhanced security.
Automate critical functions to automatically surface risk and indirect relationships, enforce dependencies and track compliance.
Track data throughout its entire lifecycle – from source to consumption – to build trust and maximize the value of your data governance.
Unearth highly predictive relationships for analytics and machine learning models to make more informed predictions and decisions.
Neo4j Customer Segmentation Analysis, 2020
Pulse Survey, 2020
Neo4j Customer Segmentation Analysis, 2020
A data management knowledge graph that aims to drive action by providing data assurance, discovery, or insight.
A knowledge graph used for analytics, machine learning or data science where the aim is to improve decisions.
With POLE [knowledge graph], what you see is what you get – there is little to no difference between our data models and conceptual models of the business problem.
Tuomas Piippo, CTO Turku City Data
Ninety percent of data scientists are using Amundsen [knowledge graph] to do their jobs on a weekly basis. We also found that this tool has increased productivity for our entire data science organization by around 30 percent.
Tamika Tannis, Software Engineer, Lyft
We used graph algorithms to find patients that had specific journey types and patterns, and then find others that are close or similar.
Joseph Roemer, Global Commercial IT Insight & Analytics Sr. Director, AstraZeneca
Using Neo4j, someone from our Orion project found information from the Apollo project that prevented an issue, saving well over two years of work and one million dollars of taxpayer funds
David Meza, Chief Knowledge Architect, NASACheck out our Knowledge Graph Quick Start service that takes you from zero to operational in as little as 8-10 weeks.
You can use our pre-built and customizable Solution Frameworks with proven code, models, and ontologies.