Text Analytics & Semantic Data Enrichment

SciBite's Text Analytics & Semantic Data Enrichment solution is designed for life sciences and pharmaceutical organisations seeking to unlock the full value of their existing document repositories. By applying sophisticated named entity recognition (NER) technology, SciBite enables teams to retrospectively process, catalog, and apply metadata to millions of documents — transforming previously unusable unstructured text into annotated, standardised, and machine-readable data that supports smarter, more informed decision-making across research, experiments, and investments.

At the heart of the solution are SciBite's NER-tuned VOCabs — highly curated biomedical terminology lists built on decades of ontology expertise. These VOCabs span life science, pharma, genes, chemical agriculture, clinical conditions, drugs, human phenotypes, and many more domains, with over 180 VOCabs already available. The result is a platform that delivers high-quality outputs by intelligently navigating complex scientific language, handling synonyms, species variations, errors, language differences, and common misunderstandings to avoid false positives and ensure truly useful enriched data.

Core Capabilities

• Retrospective analysis of large document archives, enabling organisations to tap into years of hidden research and results
• Named entity recognition that identifies, tags, and extracts relevant scientific terminology from unstructured content
• Conversion of unstructured text into annotated, standardised, machine-readable data
• Application of detailed contextual rules informed by genuine scientific expertise to minimise false positives
• Support for navigating millions of documents through expertly curated tags, annotations, and metadata

VOCabs: Ontologies Taken to the Next Level

• Over 180 pre-curated VOCabs covering genes, clinical conditions, drugs, human phenotypes, chemicals, agriculture, and more
• Developed and refined from publicly available content, reflecting deep knowledge of scientific language and its practical application
• Intelligent handling of synonyms, species references, terminology errors, and domain-specific language nuances
• Designed to link relevant sets of terms while omitting unnecessary or misleading tagging

Organisational Customisation

• VOCabs can be tailored to reflect the unique terminologies, shorthand, and house brand names used within a specific organisation
• Organisations can work with SciBite specialists or leverage in-house expertise to create and manage custom VOCabs
• Quick and easy VOCab creation process suited to both guided and self-managed implementations

TERMite NER Engine

• TERMite is SciBite's ultra-fast named entity recognition and extraction engine
• Widely compatible with existing applications and workflows
• Integrates identification, tagging, and extraction directly into existing systems
• Transforms unstructured content into organised, machine-readable data at scale
• Accessible via API, enabling scientists to search, interact with, and extract data programmatically

SciBite's Text Analytics & Semantic Data Enrichment solution is backed by a team of ontology and NER specialists who work alongside customers to ensure high-quality outcomes. The platform has been successfully deployed at top-tier pharmaceutical organisations, where it has been used to deliver end-to-end text mining services, empowering scientists with both search capabilities and data extraction through an API-driven interface.