Graphic of knowledge graphs
Art and Graphics by Laura Cattaneo for Data Language use only

Knowledge Graphs

Data Language is a market leader in the design, implementation, and deployment of knowledge graphs (KGs), and boasts the most rapid speed-to-market techniques. Knowledge Graphs are a key element of information management strategies for data driven products.
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A knowledge graph is a collation of information and data typically within the domain of your business context. The information is stored in a database, often a graph database such that it can be queried, maintained, and interacted with by humans or machines.

Data Language and Knowledge Graphs

Data Language has more than 15 years of experience in the design, implementation and deployment of knowledge graphs, linked data, and semantic web technologies. We have been working with graph databases since our inception, when our founders worked on possibly the UK’s first enterprise scale knowledge graph for BBC News and BBC Sport. Since then we have helped global organisations such as Cochrane, Wellcome, Spotify, Syngenta, SKY, NewsCorp and Euromoney implement them too.

Our typical approach to implementing a knowledge graph solution is:

1. Domain Driven Design

Through collaborative domain modelling exercises, we work with stakeholders to gain a broad understanding of the target domain, iteratively refining and validating ontology models to achieve the ideal level of detail to deliver the best outcomes, business value and utility.

2. Technical and Data Architecture

Not all graph databases are equal. We are wholly vendor-agnostic, and our deep experience in designing scalable, maintainable, and evolvable knowledge graph solutions, means we will always select the right graph technology to deliver your use-cases. We understand the difference between label-property graphs and RDF based graph databases. We have a total grasp on the practical and pragmatic use of semantics and inference to ensure your solution is not over-engineered but delivers just the right level of complexity to optimise your total cost of ownership, and ensure your solution can evolve as your business evolves.

3. Integration - Data and Software Engineering

We can help you integrate the knowledge graph with your business systems and workflows, either by helping your own software engineering team with querying the graph using query languages such as SPARQL or Gremlin, through to developing microservices and web interfaces that encapsulate your target use-cases. We are skilled at data engineering, and can help you populate your knowledge graph, transforming and loading your own business data, and linking to or ingesting open-data as required.

4. Automation and Deployment

We can work with and guide your own infrastructure team, or take on the task of engineering, integration and deployment entirely ourselves. We are skilled at contemporary best practices for deployment of infrastructure in the cloud or on-prem. Our devops engineers are skilled in the use of infrastructure-as-code for fully automating the deployment and maintenance of the entire knowledge graph solution.