Top 10 Pharma Company Gains Greater Insights Using Smart Search Across Their Siloed Structured and Unstructured Data

Ontotext created a semantic search solution for a top 10 Pharma company, enabling users to get better insights by interlinking various siloed content based on semantic rules. The solution addressed 5 diverse use cases, requiring deep analysis of the content structure, information extraction from unstructured content, and building a targeted knowledge graph by ingesting structured datasets.

    • Minimized time and resources spent on repetitive efforts
    • Gained better insights for data-driven decisions, ensuring accuracy and reliability
    • Improved user experience and increased efficiency, freeing up resources for other value-added tasks
    • Reduced risk of errors for greater trust in decisions

The Goal

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 Challenge

The Pharma company needed an intelligent industry-specific solution that provides:

  • automatic categorization and semantic sectioning of complex documents;
  • normalization of both structured and unstructured data towards ontology terms used by text analysis pipelines;
  • fusing of structured and unstructured data into a knowledge graph;
  • powerful semantic search user interface to enable seamless data exploration through the knowledge graph.

The Solution: A Smarter Semantic Search Tool for Better Knowledge Insights

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.

This is an infographic about Ontotext Pharma Use Case Semantic Search Architecture

Business Benefits

  • Intuitive and value-add semantic search based on auto-suggestions of concepts from the knowledge graph
  • Term proximity search and knowledge exploration capabilities
  • Ability to track and have provenance of all extracted facts from the source documents (with highlights and navigation within the content)
  • Semantic vectors-based similarity search (later included in Ontotext’s leading semantic graph database GraphDB), enabling automatic matching between documents or parts of documents.

Why Choose Ontotext?

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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