Are you, as a data steward, data scientist or researcher, constantly wasting time cleaning and structuring siloed data from different data sources and datasets? And does the way the data is stored and annotated hinder you in making biomedical discoveries?
Maybe you have just started looking into semantic models and knowledge graphs as a solution for these time and resource consuming efforts? This infographic shows the benefits of semantic models and knowledge graphs and how The Hyve, in collaboration with a top pharma company, solved their data integration challenge.
In contrast to other types of data models, a semantic model specifies the meaning of domain-specific concepts such as genes, proteins, and assays. With a well-integrated semantic model, both humans and machines can easily find which data is right for their use case and unambiguously understand the data and its context. A semantic model simplifies research data integration and management and forms a key step towards making data more FAIR.
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.