Life sciences and pharmaceutical enterprises are turning to informatics teams to lead artificial intelligence, cognitive search, and machine-learning initiatives aimed at exploiting vast amounts of internal and external information. In this endeavor, the informatics team focusing on applying machine analysis methods such as text mining and information management professionals examining approaches to make these insights discoverable and accessible have the same goal—delivering actionable intelligence across the enterprise.
Although the above-mentioned initiatives show great promise, the reality is often less promising because of the challenges associated with interoperability across different data streams. However, semantic enrichment can support the informatics and information management teams to advance such efforts.
For instance, if the informatics team contributes expertise in data transformation and interoperability and the information management team creates awareness of how content is discovered and accessed, then semantic enrichment can help by enhancing content with information about its meaning, thereby adding structure to unstructured information.
Additionally, semantic enrichment offers possibilities such as extracting candidate relationships between whole classes of concepts like genes and diseases, irrespective of phrasing, location, source, and format.
Click here to read how semantic enrichment reduces friction in the discovery, access, consumption, and synthesis of published scientific literature.
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