Information retrieval system for semantic searches in large text collections
SCAIView is a knowledge discovery software for the life sciences. It facilitates­ the rapid identification of aggregate­d information from large text sources. Knowledge Discovery and Semantic Search Documents are retrieved by precisely formulated questions using ontological representations of biomedical entities. The entities are embedded in searchable hierarchies and span from genes, proteins, accompanied single-nucleotice polymorphisms to chemical compounds and medical terminologies. SCAIView supports the selection of the suitable entities by an autocompletion functionality and a knowledge base for each entity. This includes a description of the entity, structural information, pathways and links to relevant biomedical databases like EntrezGene, dbSNP, KEGG, GO and DrugBank. SCAIView represents the search results using a color-coded highlighting of the different entity-classes, statistical search results and various ranking functions.

For end-users
Application, Software, Service
API Type:
Knowledge bases and data mining
Applicability domain:
Identifier mapping, Information extraction
Targeted industry:
Drugs, Chemicals
Targeted users:
Students, Researchers
Relevant OpenRiskNet case study:
DataCure - Data curation and creation of pre-reasoned datasets and searching

Provided by:
Fraunhofer Gesellschaft
Fraunhofer Institute for Algorithms and Scientific Computing SCAI Contact: More details:
Login required:
Implementation status:
Available as web service, Application programming interface available, API documentation available (Swagger-OpenAPI v2), Containerised, Graphical user interface available
Integration status:
Integration in progress
Service integration operations completed:
Utilises the OpenRiskNet APIs to ensure that each service is accessible to our proposed interoperability layer.
Is annotated according to the semantic interoperability layer concept using defined ontologies.
Is containerised for easy deployment in virtual environments of OpenRiskNet instances.
Has documented scientific and technical background.
Is deployed into the OpenRiskNet reference environment.
Is listed in the OpenRiskNet discovery services.
Is listed in other central repositories like eInfraCentral, and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.