Knowledge Graphs

Understand your data and maximize its value with a Knowledge Graph

Having all data in a structured, queryable model stimulates internal and external collaboration and speeds up research and development processes. It also enables maximum (re-)use of all available data.

With knowledge graph capability, data integration is simplified because meaning has been standardized. Processes can be automated by reducing the need for reconciliation. Data teams get analytical flexibility and the ability to ask ‘what if’ questions of the data. Data stewards can manage data more efficiently as all data points as well as the relationships between data elements are captured.

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Prerequisites

Before engaging in any Semantic Model or Knowledge Graph work, it is essential to perform a Data Landscape Exploration within your organization. This will enable us to understand which data is available and where, how to improve compliance with regulations (GDPR) and best practices such as FAIR) as well as being able to manage the full data lifecycle.

Alternatively, we can provide a Data Landscape Exploration as part of the Semantic Modeling or Knowledge Graph service. Our assistance can range from cataloging to creating an understanding of existing applications and data sources within the company and readiness for FAIR.

Process

Before building a knowledge graph, The Hyve first consults with data stewards or data managers to define business and scientific use cases. We make an inventory of the existing semantic structure and an outline of the semantic structure needed to realize the defined use cases.

With this in place, the next step is to set up a number of operational activities such as data mapping and visualization.

The Hyve can design and build knowledge graphs in the life sciences domain working with biomedical datasets.
We use tools such as Protege, TopBraid Composer, AllegroGraph, and WebVOWL - depending on the specific use case.
We can load our in-house developed model and knowledge graph into these tools and demonstrate SPARQL queries.

Having all data in a structured, queryable model speeds up research and development processes, enables maximum (re)use of the data and stimulates internal and external collaboration.

With knowledge graph capability, data integration is simplified because the meaning has been standardized. Processes can be automated, reducing the need for reconciliation. Data teams get analytical flexibility and the ability to ask ‘what if’ questions to the data. Data stewards can manage data more efficiently as all data points as well as the relationships between data elements are captured.

Knowledge Graph infographic

As a data steward, data scientist and researcher, you are constantly wasting your time cleaning and structuring siloed data from different data sources and datasets which hamper your efforts in targeting and making biomedical discoveries.

If you have just started to look into semantic models and knowledge graphs that have shown to remove these time and resource consuming efforts, you can download this infographic that shows you the benefits and how we collaborated with a top pharma to solve their data integration challenge.