## 3 7R Data Value Framework in Practice
### Data Design, Development and Deployment
#### 3.2.1 R4 (Refine): Data Rationalization, Staging and Semantics  
The transformation from semi-structured content to semantic knowledge involves the selection of an ontology that provides concepts and properties that cover the domain of interest, a definition of patterns to create dereferenceable (unique) identifiers for entities and the selection of a mapping language to map elements from non-ontological resources to terms in the ontology.  
The MAREC corpus contains nearly 20 million patents. Publications are retrieved from the PubMed Central website consisting of nearly 2.7 million articles. Funding calls and topics are acquired from EUresearch consisting of less than thousand documents for one year. For the patent domain, a specific ontology, called Patent Expert, is used that provides terms to which all of the elements and attributes in the XML document are mapped. Well-known ontologies, such as Dublin Core and Bibliographic Ontology, cover PubMed documents. A lightweight ontology is used for data about funding opportunities.  
Because the format of the MAREC and PubMed documents is XML, the EXtensible Stylesheet Language language XSLT is used to define the mapping between these documents and their RDF/XML representation. Funding opportunities are provided as non-standard CSV files for experimental purposes. The approach taken transforms structural elements of the data into RDF and then uses the SPARQL CONSTRUCT rule language to map the intermediate RDF to terms in the ontology to arrive at the final RDF data. Besides the common vocabularies (such as RDF and the Web Ontology Language OWL), Dublin Core terms are used for resource publications, and the FOAF (Friend of a Friend) vocabulary is used to represent persons and organizations. PROV-O (provenance ontology) is used for provenance. SKOS (Simple Knowledge Organization System) and XKOS (extension to SKOS) are used to cover concepts, schemes and their relationships to one another.  
Each entity (data item) mapped to RDF must also be assigned a web addresses as a unique identifier. In cases where the unique identifier can be constructed from the data source or follow a common pattern (e.g., patent IDs), they are used directly in the pattern in order to create predictable (resource-friendly) and human-friendly web addresses. In all other cases where the occurrences of things or concepts cannot be absolutely differentiated from one another (e.g., person names), universally unique identifiers (UUID) are generated.  
Three transformer engines for mapping the source data to linked data are provided as Fusepool enhancer components covering two subtasks. One subtask, data mapping, concerns the development of the transformations for the documents that are chosen as source: patents from the MAREC corpus, PubMed articles and EUresearch funding opportunities. The second subtask, RDFizers, concerns including such transformations into Java OSGi bundles in order to quickly deploy those transformations within the Fusepool platform (OSGi provides a component model for Java and is sponsored by the Open Service Gateway initiative). The first two transformations are from XML to RDF/XML and are provided as XSLT style sheets. The funding opportunities are transformed into RDF using SPARQL rules.  
**EXAMPLE: Data processing and analytics components**  
* Data life cycle (DLC): DLC implements the processing chain for data imports for RDF mapping, interlinking and smushing into the RDF content graph;
* DLC patents: Java OSGi bundle with the DLC components needed to process patent data including the service to transform patents from MAREC XML format to RDF;
* Dictionary matching algorithm: Implementation of a dictionary-matching algorithm as Fusepool enhancer engine that outputs RDF triples from extracted entities from text documents;
* SILK linking: OSGi bundle that wraps the SILK Link Discovery Framework for the interlinked service into the Fusepool platform.