The accuracy and relevance of results produced using an RAG model are ultimately limited by the quality and relevance of the documents that are retrieved during the search process. This means that if your documents aren’t properly annotated and optimized, there’s still a real probability that you will miss vital information. Using ontologies to enrich these documents and support your searches will unlock higher levels of accuracy, relevancy and transparency.
Using TERMite from SciBite, you can create highly performant, NER-optimized vocabularies with the power to turn complex documents into machine-readable data.
Raising the quality and relevance of documents with SciBite enables scientists and researchers to identify the correct information on a much more consistent basis.
Traditional searches rely on users inputting exact words, phrases and terms. SciBite uses ontologies and AI to add context to reveals results that may have been missed.
This can be a big gap, but ontologies form part of the bridge. SciBite uses ontologies to act as translators between these languages.
By clearly defining terms and how they’re connected, we can enrich documents with the metadata to complement vectorization. This makes it easier for a computer to understand the human’s goal (expressed as a concept or idea) and together with LLMs, deliver enhanced accuracy, improved transparency and greater consistency in information retrieval.
Powered by an advanced matching algorithm, SciBite goes beyond simply selecting overlapping entities to consider the proximity, occurrence and hierarchical relationships between the entities in documents as well as your search terms.
Greater transparencyOntology enrichment enhances transparency by empowering users with detailed information and explanations throughout the document retrieval process.
More flexibilitySciBite not only enables you to use ontologies to enrich private data with internal vocabularies, but it also allows you to take this terminology and apply it to a range of external data sources too.
The best of bothVector-based search can deliver high levels of accuracy in a very effective manner, using this methodology with ontology-enriched documents gives you and your team the chance to heighten the strengths of this method.
Watch and listen more about how SciBite is using LLMs and AI to take data enrichment and searching to the next level.
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