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:
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.
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.
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.
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.
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.
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.