A knowledge graph is composed of a graph database to store the data and a reasoning layer to interpret and manipulate the data. RDFox is an in-memory, Resource Description Framework (RDF) triple store.
RDFox stores data in triples. A triple represents three linked data pieces, i.e. subject-predicate-object, often referred to as nodes (data point i.e. subject or object) and edges (the relationship between them).
But why a knowledge graph rather than a relational database? We’ve asked the team!
“Graph databases offer a more flexible approach to storing data than the traditional relational database” CEO
Relational databases require data to be stored within a strict table structure, where columns represent attributes and rows hold a recorded value for the attributes. Ironically, the rigidity of this may result in the loss of reported relationships between stored data points.
Knowledge graphs overcome the flexibility limitations of relational databases, allowing data points to be encoded as richly connected entities.
“Knowledge graphs provide more flexibility for modelling data and deliver great performance” Senior Software Engineer
Knowledge graphs provide a more expressive data model. The ability to assign or derive meaning from information represents a technological advance.
“It is natural that the industry shifts from Relational databases to Graph databases, as they unlock more potential” Senior Software Engineer
The size of the computer is irrelevant if the database itself limits the access or exploration of data. Graph databases allow simple and fast retrieval of information using queries. RDFox uses the standard SPARQL query language, which allows rapid data retrieval, even for heavily inter-connected data.
“RDFox delivers on the performance and speed issues that other graph database solutions have struggled with, whilst providing semantic reasoning, a feature which was designed from the bottom up” CEO
The novel design and concepts that underpin RDFox guarantee the correctness of query results, indicating significant advances on alternative solutions. This is the result of mathematical validation and peer-reviewed research, undertaken at the University of Oxford.
“RDF structures open the door for effective reasoning, which other graph database formats simply cannot compete with” Senior Software Engineer
The highly effective nature of graph databases for retrieving, querying and modelling data mean that the applications of such a system are superior to their relational-backed predecessor.
“Graphs are going to be the next standard in databases” Knowledge Engineer
The ability of businesses to use the data they collect or create effectively, is currently low. In a world where data is valued so highly, if companies could unlock the full potential of their data, we would surely see some significant advances.
RDFox has the capabilities to effectively access, connect and query data, unlocking its true value. To read about how RDFox helped Festo drastically improve their configuration automation services read our case study here:
Case Studies - Oxford Semantic Technologies
This case study will show how Festo, a worldwide leader in automation and a world market leader in technical training…
Click here to request an evaluation or demo. For more information on knowledge graphs and semantic reasoning engines read our Towards Data Science article. To set up RDFox, read our getting started guide. For further information on how RDFox could help your business, contact us at email@example.com.
Team and Resources
The team behind Oxford Semantic Technologies started working on RDFox in 2011 at the Computer Science Department of the University of Oxford with the conviction that flexible and high-performance reasoning was a possibility for data intensive applications without jeopardising the correctness of the results. RDFox is the first market-ready knowledge graph designed from the ground up with reasoning in mind. Oxford Semantic Technologies is a spin out of the University of Oxford and is backed by leading investors including Samsung Venture Investment Corporation (SVIC), Oxford Sciences Innovation (OSI) and Oxford University’s investment arm (OUI). The author is proud to be a member of this team.