Open Link Data System

With Ontotext Insights Platform, a private Linked Data Cloud is easy.  Any one can easily build a semantic search and exploration system over the data they need.

The platform eliminates the technical obstacles usually faced when building a Linked Data System and significantly reduces the time for development of prototypes and fully functional applications based on the Linked Open Data.


Although “Linked Data” has grown in popularity and many academic and commercial projects have explored this promising technology, there is still substantial effort in the development of production applications based on the linked data approach. Despite the fact that many public sources are distributed in a “linked data” ready format, it is still a challenge to build a usable linked data:

  • Gaps in the data – Still there is limited coverage of RDFized data sets for particular sub-domains. This makes the building a comprehensive description of the entity class a very hard task.
  • Different Schema modeling – The data in the different data sets is not modeled in the same way although it describes similar entities and their properties. This might cause significant redundancy problems and contributes to the lack of comprehensiveness problem.
  • Instance mappings – Most data providers do not follow any common guidelines for publishing and interlinking structured data across different data sets, which makes the automatic generation of linked data and combining a collection of RDF-ized data sets impossible.
  • Unstructured data – Data sets still contain huge amount of unstructured data which can not be used for building of Linked Data.

Because of the imperfections in the available RDFized data, just loading multiple RDFized data sets in a repository is not enough to build a useful linked data cloud. This obstacle significantly increases the resources required for linked data system prototyping and lowers the confidence in the added value that the technology could bring to the business.


  • Flexible offering – pay only for components that you need and use
  • Quick setup and instant usage
  • Up-to-date data – all data sources are regularly updated with the latest releases
  • Unlock the knowledge hidden in unstructured texts – enrich the Linked Open Data with knowledge discovered in documents
  • Expert custom support – Do you need to bring your internal data to the Linked Open Data cloud? Our experts will help you make this happen!
  • Quality of service guaranteed by a SLA

Semantic Indexing and Search

Linked Life Data Platform delivers all the required functionality to enable semantic search over all the data loaded in the system. The platform provides an intuitive administrative user interface for definition and precise tuning of faceted semantic search, powered by auto-suggest, multiple facet filters and cross-entities search functionalities.

Data Rendering

Analysis and effective use of Linked Data still lags far behind its generation rate. The challenges of visualizing, browsing, and analyzing Linked Data are mostly limitations of its native format, which might be appropriate for computer-computer interactions, but not for user-computer interactions.  The platform provides standard data rendering modules which can visualize the data from any supported data set in user-friendly formats. In addition LLD platform allows the definition of more complex dashboards which can aggregate data from multiple data sets and integrate it with basic analytics capabilities.

Custom Services

If using the Linked Open Data cloud is not enough for your case and you need to extend it with internal/in-house generated data or other public sources of information, our team of domain specialist could provide you with tailored for your needs professional services. We can integrate any semantically related data set and develop custom semantic annotation pipelines to extract and semantically model important information locked in your internal unstructured data.


Linked Life Data Platform adopts Ontotext’s Insights highly advanced methodology for Linked Data generation.

All supported data sets are transformed to a common RDF data format, keeping in mind the semantic consistency at both the schema and instance levels.

All closely related data sets are grouped into Linked Data Hubs, each describing a specific class of biomedical information – disease, target, drug, patient, etc.

Although that the information integrated within a data hub might originate from many different sources and is kept in its original schema, the redundant data is clearly identified and used in terms of different provenance sources, thus improving the reliability of the information.  Linked entities and their semantic relations are used to interlink the different Linked Data Hubs, thus making semantic bridges between them.

Linked Life Data – Show case

Based on Insights Platform, Ontotext developed the Linked Life Data public service. This service provides access to 25 public biomedical databases through a single access point. The service allows writing of complex data analytical queries, answering complex bioinformatics questions such as “give me all human genes located in Y-chromosome with known molecular interactions”. You can try the service for free on the Linked Life Data Site.


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