One of the largest global Pharma companies needed to build a semantic search tool that would enable its users to easily find relevant information across large volumes of siloed structured and unstructured data sources.
Their existing solution couldn’t address data access and sharing needs efficiently as finding historical data in different documents took significant time and resources. There was also a high rate of repetitive errors coming from the lack of proper knowledge sharing and use of historical data.
The Pharma company needed an intelligent industry-specific solution that provides:
The semantic search solution provided by Ontotext enables the Pharma company’s users to get better knowledge insights by interlinking various siloed content based on semantic rules. The different use cases required deep analysis of the content structure, information extraction from unstructured content (Health Regulatory documents, SOPs, technical manuals, etc.), and building a targeted knowledge graph with the ingestion of structured datasets.
Currently, Ontotext’s solution provides easy access to relevant information across huge volumes of data. It cuts on time and resources, minimizes errors, and improves user experience.
All five use cases were successfully implemented and two of them were nominated for the next phase of the adoption plan for semantic technology within the company.
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