Global Pharma Company Enables Semantic Similarity Search in Documents

A global Pharma company can quickly process large volumes of Regulatory questions and scale up information extraction using Ontotext's AI-powered semantic similarity search offering

  • Decrease response time for Regulatory Authorities questions from 2 days to less than 1 hour
  • Increased efficiency by providing full access to a comprehensive collection of Q&As
  • Improved user experience and confidence in accuracy

The Goal

A global Pharma company selected Ontotext to create a smart industry-specific solution for processing the large volumes of diverse questions from Regulatory Authorities delivering answers in a quicker response process.

Although the company had amassed a large archive of questions answered previously, the existing solution could not handle this process efficiently. The different formats and the various document management platforms that had stored the Q&As over the years made it very difficult to reuse the company’s knowledge. Even when answering repetitive and very often identical questions about the same product, company analysts regularly had to spend days searching for the answers.

The Challenge

One of the main challenges for the Pharma company was that the system being used was based on conventional search technologies and most of their documents were in an unsearchable PDF format. Complicating this further, these documents were not indexed and the provided metadata was fragmentary and of poor quality.

Another difficulty was that to find relevant documents, the analysts had to write a series of complicated queries, trying to match keywords from the new question to keywords from existing documents containing answers. This was a complex, iterative process of figuring out how to make the right level of query specificity, in order to yield meaningful results.

During this process, the analysts had to review long lists of results, weed out duplications and unrelated items, and determine which (if any) of the documents would best serve their purposes. This method was time-consuming and required years of expert knowledge, which also made onboarding of new employees a demanding task.

The Solution – KG-powered Semantic Similarity Search

Ontotext’s smart semantic similarity search solution enables the Pharma company to quickly process large volumes of Regulatory questions and scale information extraction.

The Solution - KG-powered Semantic Similarity Search

The solution ingests the various documents from the company’s archive and automatically extracts and categorizes Q&A pairs. The content of questions is semantically indexed, so that the system can compare new questions to all previous ones, even when formulated differently (from a partial inversion or deletion to more significant alterations).

The processed data is used for building a knowledge graph that represents the relations between the different elements of the document. Empowered by this knowledge graph, Ontotext’s GraphDB’s semantic text similarity search is used to match words that co-occur with other words in the same context. (For example, even when “cancer” and “metastasis” appear in different texts, they can still be matched as semantically related.)

Finally, Ontotext’s solution returns the top 10 most similar Q&A pairs from the archive, so now company analysts only need to review them and, if necessary, make modifications before sending their answer. They can also increase the weight of specific terms within a query to the system (to focus, for example, on the safety aspect of a question) and narrow down the results even more.

Business Benefits

  • Full access to a comprehensive collection of Q&As
  • Easier to identify similar questions and their relevant answers
  • Ability to reuse the company’s knowledge by simply copying and pasting answers from previous questions
  • Responding much faster to questions – from 2 days to less than 1 hour

Why Choose Ontotext?

With Ontotext’s smart semantic similarity search solution, the Pharma company analysts can increase their efficiency, cut on time and resources, and stay in compliance.

Ontotext’s solution was built for a specific Pharma Regulatory problem, but the functionality is applicable to all types of domains as it is based on generic technology.

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