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SciBite / Use Cases / Departmental Search

Departmental search Science-based research companies and cross-functional project teams rely on the knowledge contained within the documents, reports, emails and articles they procure over time.

The challenge is that the unstructured nature of these files, together with multiple formats, makes it difficult for teams to mine them for information.

Document management systems only go so far in helping to organize these files. Search capabilities are usually limited to authors and exact content matches, which isn’t enough to cope with the inconsistent terminology and naming conventions often used during data entry. SciBite gives your team the tools they need to semantically enrich their documents, making it simpler to mine data and get the valuable business and scientific insights they need. 
How SciBite reveals the value within scientific documents
Icon - Automated processing
Automated processing

SciBite can ingest a wide range of file formats, including the batch loading of zip files by polling a location for new content, saving time and reducing the risk of administrative error.
Icon - Ontology management
Ontology management

CENtree enables teams to maintain ontologies applied to entities while machine learning can suggest potential new terms based on similar context and usage.
Icon - Semantic indexing
Semantic indexing

SciBite’s named entity recognition engine TERMite applies ontologies to generate a semantic index to enrich and structure data. 
Overview The challenge of vast amounts of inaccessible data hidden away in scientific documents is often compounded by the variety of different formats and the locations that it’s stored.

SciBite’s solutions transform unstructured text from Word documents, PDF and PowerPoint presentations using semantic indexing to create a unified structure that can be queried by a built-in user interface or a third-party search and visualization tool. 
Summer Meadow
Intelligent search

SciBite enables teams to perform searches for terms that co-occur within a sentence or document. This can create new avenues for research by generating a list of entities (i.e., genes) that are most frequently mentioned within a topic of interest (diseases).

 Ontology-based questioning

SciBite makes it easier to interrogate data, as it retrieves all the relevant information within documents regardless of the query term or synonym used. This means it can handle more complex ontology-based queries and return results from across data sources.

 Faster insights

As SciBite accurately marks up all relevant terms and concepts, teams can quickly gather summaries from projects or studies. This information can be presented to users as Spotfire dashboards, Linkurious network views and other such tools, to deliver an overview of the study or project without having to read all the associated documents.

 Holistic overview

In addition to document data, teams can apply semantic enrichment to other internal sources, databases, patents and clinical trials data to better understand their competitive position in relation to their project work.
Take a closer look

See how SciBIte can help you unlock your department’s data potential.  Download the full use case here.

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Relevant resources, events and news
https://scibite.com/knowledge-hub/resources/identifying-internal-expertise/ thumbnail image Resource Identifying internal expertise [Use Case]

SciBite enable companies to index content created by internal experts & build a profile of relevant terms that reflect their research activity

 TERMite Enterprise Fair Data

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https://scibite.com/knowledge-hub/resources/semantics-in-enterprise-search-use-case/ thumbnail image Resource Semantics in enterprise search [Use Case]

Using semantic enrichment we bring scientific understanding to enterprise search, to understand scientific concepts within unstructured text

 TERMite

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