What is a knowledge graph?

A knowledge graph is a collation of information that describes important entities and their relationship with one another. The information is stored in a database, often a graph database, in a way that it can be queried, maintained, and interacted with by humans and machines. Importantly, a knowledge graph supports powerful business use cases because the information and data within it carries both context and meaning, wherever it is consumed.

Why is everyone excited about knowledge graphs?

Knowledge graphs are becoming the preferred approach to information management because they are so easy to use for both humans and machines. Business users in general are excited because with the right tools knowledge graphs can be a thrilling and rewarding way to explore, manage, and interact with business information. Leaders are keen to use knowledge graphs because they encourage strong and efficient information management, and prepare organizations for automation and AI services. B2B and B2C customers react well to services built on knowledge graphs because, with the right model design, the services are intuitive and useful, due to the context and meaning exposed from the underlying knowledge graphs.

What are the top factors driving growth of knowledge graphs?

The need for businesses to manage and extract insights from large and complex datasets in a way that makes sense to humans and machines. The increasing demand for personalized experiences that are best delivered when machines and humans understand one another. The need for interoperability and data sharing across different systems, which heavily relies on the semantic nature of knowledge graphs and the portability of their information.

Knowledge Graphs

LLM and Knowledge Graph Hack Event with KCL & Turing Institute

Integrate a Large Language Model (LLM) AI with a Knowledge Graph

This week, Data Language is joining researchers, practitioners and experts in a Kings College London and Alan Turing Institute hack event focused on LLMs, Generative AI and Knowledge Graph tech.

Matt Shearer

August 7, 2023

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 knowledge graph models 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. 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.