Intelligent Scientific Search
Enabling better data discoverability by leveraging semantics for intelligent scientific search.
- SciBite
- SciBite Search
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SciBite Search
SciBite’s next-generation scientific search and analytics platform offers powerful interrogation and analysis capabilities across both structured and unstructured public data and proprietary sources.
Utilising SciBite's robust suite of ontologies, SciBite Search automates semantic enrichment and annotation, transforming unstructured scientific text into clean, contextualised data free from ambiguities like synonyms or cryptic data such as project codes and drug abbreviations. It supports a wealth of features including marking-up PDF documents, support for federated searches and natural language queries tailored to the needs of individual users.
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The Evolution of Search
Traditional keyword search identifies literal matches for a query. Semantic search extends this with an understanding of the concepts and context of a query. Learn how SciBite Search builds upon these foundations, using knowledge graphs to augment searches with the structure and relationship between them.
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FAIR Data Principles
The explosion of data in life sciences is leading many organisations to review their approach to data stewardship in an effort to extract maximum return from their research investment. Learn how the combination of ontologies, deep learning and FAIR data can provide a solution to this challenge.
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Advanced Search Queries
SciBite Search enables users to get accurate results without the need to understand the complexities of TERMite: our Named Entity Recognition (NER) engine. Learn how SciBite Search combines powerful search capabilities with an intuitive user interface.
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Product Highlights
SciBite Search features federated search for open source and proprietary data, together with connectors and parsers for different sources and content types. Both document and sentence-level searches are supported, plus full document-level security including role-based access.
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The Way Ahead
SciBite Search development roadmap will include seamless access to additional insights, answers and content sources together with more augmented search, question-answering capabilities and connectors for both structured and unstructured data sources.
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The Evolution of Search
Traditional keyword search identifies literal matches for a query. Semantic search extends this with an understanding of the concepts and context of a query. Learn how SciBite Search builds upon these foundations, using knowledge graphs to augment searches with the structure and relationship between them.
-
The Evolution of Search
Traditional keyword search identifies literal matches for a query. Semantic search extends this with an understanding of the concepts and context of a query. Learn how SciBite Search builds upon these foundations, using knowledge graphs to augment searches with the structure and relationship between them.
-
SciBiteSearch
SciBite’s next-generation text analytics and search tool offers powerful interrogation and analysis capabilities across structured public data and both structured and unstructured proprietary sources.
SciBiteSearch includes a wealth of features that will transform your search experience, including marking-up original format documents such as PDFs, federated searches, together with natural language queries that can be tailored to your individual needs.
-
The Evolution of Search
Traditional keyword search identifies literal matches for a query. Semantic search extends this with an understanding of the concepts and context of a query. SciBiteSearch builds upon these foundations, using knowledge graphs to augment searches with the structure and relationship between them.
-
Advanced Search Queries
SciBiteSearch offers powerful search capabilities with an intuitive user interface. Users can undertake basic keyword queries or employ our advanced query language to answer more complex questions.
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Product Highlights
SciBiteSearch features federated search for open source and proprietary data, together with connectors and parsers for different sources and content types. Both document and sentence-level searches are supported, plus full document-level security including role-based access.
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SciBiteSearch vs DOCstore
SciBiteSearch's new architecture allows you to better leverage our strengths in vocabularies, natural language processing and AI to identify critical biomedical concepts and relationships as rapidly as possible.
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The Way Ahead
SciBiteSearch development roadmap will include seamless access to additional insights, answers and content sources together with more augmented search, question-answering capabilities and connectors for both structured and unstructured data sources.
-
SciBiteSearch
SciBite’s next-generation text analytics and search tool offers powerful interrogation and analysis capabilities across structured public data and both structured and unstructured proprietary sources.
SciBiteSearch includes a wealth of features that will transform your search experience, including marking-up original format documents such as PDFs, federated searches, together with natural language queries that can be tailored to your individual needs.
-
SciBiteSearch
SciBite’s next-generation text analytics and search tool offers powerful interrogation and analysis capabilities across structured public data and both structured and unstructured proprietary sources.
SciBiteSearch includes a wealth of features that will transform your search experience, including marking-up original format documents such as PDFs, federated searches, together with natural language queries that can be tailored to your individual needs.
Introducing SciBite Search
Pharmaceutical and biomedical professionals need to unravel enormous amounts of data, get fast insights from data that's locked up in both internal and external documents and can't afford to miss any crucial details.
Watch the video to find out how our intelligent scientific search platform, SciBite Search, enables researchers to quickly find meaningful insights from structured and unstructured public and proprietary biomedical data.
The Evolution of Search
The diagram illustrates how SciBite Search better addresses scientific search using Fingolimod as an example. Fingolimod (FTY720) is an FDA approved immunomodulatory drug for treating multiple sclerosis, also sold under the brand name Gilenya.
Traditional keyword search relies on a user providing all the names ("strings") by which Fingolimod is known. These are then matched to text found in the document corpus. Semantic search goes further, adding the concept of entities (things) including classes and relationships (taxonomy). Augmented search further enriches this data, enabling users to find results beyond what is inherently present in the data. Deep learning approaches are designed to handle more challenging questions and address issues such as imprecise search terms and badly indexed data.
FAIR Data Principles
The explosion of data in life sciences is leading many organisations to review their approach to data stewardship in an effort to extract maximum return from their research investment.
FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles aim to promote the integrity and re-use of scientific data. However, up to 80% of this data is stored as unstructured text such as Word documents and PDFs. At SciBite, we believe that the combination of ontologies, deep learning and FAIR data provide a powerful solution to this challenge, and our standards-based semantic tools enable FAIR data across the entire enterprise.
Advanced Search Queries
SciBite Search enables users to get accurate results without the need to understand the complexities of TERMite: our Named Entity Recognition (NER) engine. TERMite finds mentions of genes, diseases, drugs, companies, processes and chemicals within a document corpus. Co-occurrence matrices help users explore the resulting datasets and quickly identify relationships across thousands of related concepts.
SciBite Search offers powerful search capabilities with an intuitive user interface. Users can undertake basic keyword queries or employ our advanced query language to create more complex questions.
Product Highlights
SciBite Search offers a variety of domain-specific and generic connectors making it simple to load both open source and proprietary data. Built-in parsers also make it easy to render popular datasets and make formats searchable. Both document and sentence-level searches are supported, plus full document-level security including role-based access. SciBite Search has several API endpoints for integration with other key research systems.
SciBite Search allows organisations to incorporate their own branding via UI themes, colour schemes and logos. Personalisation settings provide tailored support for expert users and those who require less sophisticated functionality.
The Way Ahead
The SciBite Search development roadmap will focus on several key areas with the objective of becoming the best biomedical research solution for your department or small enterprise.
Our plans include providing seamless access to additional insights, answers and content sources together with more augmented search, question-answering capabilities and connectors for both structured and unstructured data sources. We will also have deeper integration with SciBite AI models and SciBite’s CENtree ontology management solution so that customers can leverage our full ecosystem of tools.
SciBite Search 2.2 contains:
- Includes upgraded versions of TERMite, and VOCabs
- New TSV export of search results
- More intuitive Help pages
- New sFTP connector
- Easier data ingestion and processing with option to extend (create a new copy of) an existing schema
- Simpler loading of individual SciBite Search assets (e.g., Help, vocabularies) for improved initialization/updates
API
- Expansion of asynchronous API endpoints for better handling of large export jobs
- Configurable API throttling to manage heavy API use
- More secure and flexible API client generation/management
Leverage our Experience of Semantics for Intelligent Scientific Search
At SciBite we have experience in developing and deploying semantic deep learning models that perform a wide variety of functions:-
Electronic Lab Notebooks
Semantically enrich the data stored within your ELN and mine experimental data more effectively.
Learn more
Phenotype Triangulation
Predict disease relationships based on the strength of indirect evidence.
Learn more
Pharmacovigilance
Maintain an up-to-date and comprehensive awareness of drug safety signals.
Learn more
Departmental Search
Unlock the wealth of information managed within your departmental documents.
Learn more
Competitive Intelligence
Gain early insight into potentially groundbreaking scientific advances.
Learn more
Internal Expertise
Uncover valuable insights about the latent expertise that resides within an organisation.
Learn more
Take the next step...
Contact us to discuss your requirements or read a more in-depth description of SciBite Search
