Machine Learning

ML-powered SciBite Launches AI-driven Semantic Search Platform

Intelligent scientific search platform, SciBiteSearch, enables researchers to quickly find meaningful insights from structured and unstructured public and proprietary biomedical data

SciBite, an Elsevier company and award-winning semantic technology company, today announced the launch of SciBiteSearch. The next-generation scientific search and analytics platform offers powerful interrogation and analysis capabilities across unstructured and structured data, from public and proprietary sources. Researchers today face increasing challenges around accessing and deriving meaningful insights from the ever-larger volumes of data, presented in an array of formats from multiple sources. SciBiteSearch provides scientists with access to domain specific ontology and AI-powered search capabilities, allowing users to connect and build knowledge from their data.

“Biopharmaceutical companies depend upon access to and understanding of data to advance R&D. Yet today, many data assets remain siloed,” commented Phil Verdemato, Head of Software Engineering, SciBite. “Compounding this issue, is unlike other industries where it is simply the amount of data that is the problem, it is also the variety of data streams in life sciences that presents a barrier. This makes harmonisation and comparison an uphill battle unless intelligent, purpose-built search tools are in place. The expertly tuned scientific search engine, SciBiteSearch, helps organisations address this and tackle the ‘Find’ aspect within the FAIR guiding principles for data management and stewardship.”

SciBiteSearch goes beyond traditional search methods, using knowledge graphs to augment searches and deliver not only items relevant to the query but the structure and relationship between them. The addition of AI further enhances the search experience enabling natural language understanding. SciBiteSearch can integrate data across a range of use cases including:

  • Unify multiple data sources into a single solution, designed for departments wanting their own tailored search tool. For example, combining public biomedical literature, clinical trials, and grants with proprietary data to facilitate smarter searching.
  • Incorporate full-text biomedical literature from publishers to better address researchers’ discovery needs. For example, users can load subscribed licensed data from partner publishers or content brokers.
  • Enable users to get accurate search results without the need to understand the complexities of Named Entity Recognition (NER), its underlying data structures, or the functions required to surface.

Building on the easy-to-use search system in DOCstore, SciBiteSearch offers an intuitive user interface, and sophisticated query and assertion indices created using SciBite’s tools and ontologies. A streaming load API, connectors, and parsers for different sources and content types make it simple to load and process content to make it searchable.

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