• Ontario

    Ontario is an ontology-based data integration and semantic enrichment on-demand framework over Semantic Data Lakes. Ontario adds a semantic layer on top of the source datasets which are stored as a raw format in a Data Lake. Ontario supports different data models (structured and semi-structured) such as Relational, CSV, TSV, JSON, XML, Document, and Graph. In addition, the following data management systems are supported: MySQL, Postgres, MongoDB, Neo4j, and distributed file systems Hadoop HDFS and S3. SPARQL is the global query language and currently, RML mappings are supported. URL:


    FALCON is an entity and relation linking framework over DBpedia able to identify relations and entities in short texts or questions. URL:

  • RDFizer

    SDM-RFizer an interpreter of mapping rules that allow the transformation of (un)structured data into RDF knowledge graphs. The current version of the SDM-RDFizer assumes mapping rules are defined in the RDF Mapping Language (RML). The SDM-RDFizer implements optimized data structures and relational algebra operators that enable efficient executions of RML triple maps even in the presence of Big data. SDM-RDFizer is able to process data from Heterogeneous data sources (CSV, JSON, RDB, XML). The latest version of SDM-RDFizer, version4.0, empowered by new optimization features to create very large KGs efficiently, is released in October 2021. URL:


  • Dragoman

    Dragoman is an optimized interpreter of mapping rules (defined in RML) and integrate data pre/post-processing functions defined according to FnO (Function Ontology) as part of the transformation of data into RDF knowldge graph. Dragoman enables users to provide their own required function library easily. URL:

  • easyRML

    easyRML facilitates the creation of RML mapping rules. easyRML provides a user-friendy interface enabling users to create their mapping rules without being concerned about the syntaxes of the mapping language. easyRML allows users to upload their ontology and data fields list so to have a better overview of the components of the data integration system during the process of mapping rules declaration. URL:

  • Leibniz Data Manager

    The TIB Data Manager prototype was developed to support the aspect of better re-usability of research data. URL:

  • DeTrusty

    DeTrusty is a federated query engine. At this stage, only SPARQL endpoints are supported. DeTrusty differs from other query engines through its focus on the explainability and trustworthiness of the query result. URL:

  • Trav-SHACL

    Trav-SHACL: a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Trav-SHACL reorders the shapes in a shape schema for efficient validation and rewrites target and constraint queries for fast detection of invalid entities. The shape schema is validated against an RDF graph accessible via a SPARQL endpoint. URL: