## 3 7R Data Value Framework in Practice
### Data Design, Development and Deployment
#### 3.2.2 R5 (Reuse): Advanced Enrichment and Predictive Analytics  
Reusing data describes the work done on enrichment of the data and the interlinking of different data sets. Interlinking of two entities that were computed as being the same establishes predicates such as \"same as\" (owl:sameAs), \"see also\" (rdfs:seeAlso) and \"is format of\" (dcterms:isFormatOf), between these two entities, more precisely, between their dereferenceable (unique) identifiers. Each entity (e.g., author) mentioned in the document and digested by the RDFizer is assigned a universally unique identifier (UUID). The same entity found in another document may be assigned a different UUID, because the RDFizer does not have enough contextual information to determine whether the two entities are indeed the same. In order to connect different representations of the same entity, extracted from different documents and processes, reconciliation or interlinking is required. In linked data, interlinking has a more precise meaning as the process in which a resource of a triple in a dataset (e.g., subject) is linked through a predicate (e.g., owl:sameAs) to an entity in a different dataset (e.g., object). The purpose of interlinking is to connect two or more different representations of the same entity and merge the predicates about them to augment the knowledge about it (hence enrichment). To uniquely identify each entity type, criteria must be defined based on the available information. After an element of an XML document is mapped to an ontological term, its value can be used in a meaningful way to disambiguate an entity. One such term is the type of the entity that states which class the entity is member of (e.g., patent). The type predicate is particularly useful when the classes of which the entities are members are disjoint, so that an entity cannot be member of more than one of those classes. Entities of type person, that are found in patents for instance, have other properties, such as name, country and which patents for which they are the inventors. A description based on properties, like name, affiliation and publications, can be sufficient to disambiguate two persons within the publications dataset, and a description based on properties, like name, patents granted and organization, can be sufficient to disambiguate two persons within the patents dataset. A problem arises when disambiguating two entities coming from two different datasets, such as patents and publications. To interlink entities from very different datasets, only values of properties that have the same semantics can be compared. In this case, the properties to be compared are a subset of the properties in the preceding descriptions, in order to avoid false negatives. On the other hand, we have to state whether these common properties are enough to disambiguate those entities in order to avoid false positives. In the case of patents and publications, we want to interlink different representations of the same entity in the two datasets, such as inventors and authors. In this case, the semantically-equivalent properties that can be used to disambiguate persons are type and name. Using only these two properties will lead to ambiguities, as the comparison will result in false positives in the case of homonymy, and a human intervention will be needed for disambiguation. Other properties can be used to help in disambiguating two representations of the same entity, even if they are not directly related to the entity. Inventors of a patent are related to an organization that is the applicant of the patent. Authors of a publication have an affiliation to an organization. The names of the organizations can be added to the descriptions of persons to improve the precision of the comparison. The interlinking process is implemented in Fusepool as a component that compares an entity's description extracted by an RDFizer with descriptions of entities stored in the Fusepool data store.