A knowledge graph is a network of information that blends application data and domain knowledge to provide intuitive management and improved analytics. Its flexible, deeply interconnected framework makes it ideal for the complexities of real-world data-driven use cases.
A limitless technology, RDFox is used in a wide range of applications across all sectors; inward-facing purposes such as regulation compliance and configuration management, as well as user-facing applications like semantic search and secure on-device personalized recommendations. With RDFox, our clients have been able to unlock the full potential of their data, achieving more that they ever thought possible.
There are several advantages to using a knowledge graph database; from its intuitive and flexible structure to its capacity to integrate real-world datasets and return intelligent insights. While ease of operation and actionable intelligence are vital, knowledge graphs offer one benefit that allows users to take these to another level: semantic reasoning (otherwise known as rules-based AI).
Knowledge graphs have the capability to reason over the data within. Semantic reasoning infers the logical consequences of a set of user-defined rules that enrich the dataset and allow even more value to be extracted from it. Rules offer ultimate flexibility and endless possibility—they can transform data, conduct analytics, or integrate knowledge with existing information.
Enterprise solutions don’t just require advanced functionality, they require results at speed. As the world’s fastest knowledge graph and rules engine, RDFox is currently enabling corporations across the globe to solve their business problems with graph technology. RDFox’s knowledge graph also offers the exceptional security, stability, and scalability that is required in many of its applications, giving our clients confidence in their solutions.
A knowledge graph is a valuable resource for any business as it combines diverse data sets, able to harmonise domain knowledge and contextual information. This enables businesses to gain deeper insights into their data, driving business decisions or streamlining data-based tasks. As the name suggests, knowledge graphs store data in a graph structure, consisting of nodes and their relationships (known as edges). This comprehensive approach provides a holistic view while preserving intricate details.
Organisations populate their knowledge graph with data specific to their use case, often generated through the use case itself or by their own design. Additionally, users may define an ontology or taxonomy, establishing a hierarchical structure of terms that adds domain knowledge to the data.
With RDFox, data can be seamlessly added, removed, or modified incrementally, eliminating the need to restart the system as the data changes. RDFox implements an extended version of the query language SPARQL, enabling the execution of complex queries with advanced features such as aggregation and negation as failure, providing businesses with powerful analytical capabilities.
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