Graph.Build

At Graph.Build we love knowledge graphs and we want to promote their wide-spread adoption. To do this we remove the barriers to implementing a knowledge graph which were previously cost, time and resource. We found that 90% of the time taken in getting a knowledge graph from idea stage to production was spent in what we call the ‘graph engineering lifecycle’. This lifecycle was heavily reliant upon software engineers (and related functions such as testers and devops specialists working with the subject-matter experts and ontologists who were designing the graph model.

In the graph engineering lifecycle each new knowledge graph implementation involved the same repeatable steps that took a lot of time and cost a lot of money. Once a conceptual model was designed, data needed to be transformed from its source into data that could be used in the graph model and then integrated into the model. This would be done using bespoke applications built for the specific model and source data it was to run on. When the model was tested and, invariably required refinement, this whole time-consuming process needed to be repeated.

With Graph.Build you can easily link your source data into the visual graph modelling tool and use that data to construct the graph model with a drag-and-drop approach. Once you are happy with your conceptual model you can then test it with the real data and if you need to change something, its simple. Once you are done with this stage you can load your model and data into your chosen graph database with the click of a button, ready for QA and deployment to production.

In summary, the platform exists to make knowledge graph construction more efficient and, therefore, more accessible so that more businesses can start harnessing the power of fast, complex queries on large sets of linked data to do great things.