Developing bespoke semantic queries to enable comprehensive monitoring [Use Case]

Text analytics + semantic enrichment

The Business challenge:

In an attempt to identify articles of interest amongst the background ‘noise’, LifeArc’s Scientific Horizon Scanning Team members used to manually scan through PubMed, grant information and a range of biotech-focussed news websites for potentially interesting articles. This process was resource intensive, limiting the coverage, depth and frequency of review possible.

The SciBite solution:

To provide the foundation for semantic enrichment and to complement SciBite’s existing vocabularies, we created several new vocabularies containing bespoke terms tailored to LifeArc’s business, including those relating to novelty and specific technologies such as biomarkers and diagnostics.

Multiple relevant terms and entities were combined to create semantic search patterns. To accommodate the reality that there are often multiple ways of describing the same thing, multiple patterns were aggregated into ‘bundles’ which can quickly and easily refined and run across the same data simultaneously. The number of patterns that match a piece of text and whether those matches are complementary or competing, provides a powerful and clear indicator of the relevance of search results.

An example of a ‘bundle’ of search patterns

Key business benefits:

  • Cast a wide net and reduce the time spent mining for data by up to 80%
  • Remain responsive to the evolving language in new scientific fields

Read our article Climbing Mt Peer Review: May AI help you with that? for more information.

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