# Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives
## 6 Related Work
Additionally, to emphasize the centrality of Wikidata for the Cultural Heritage world, Europeana published the list of practical steps data providers can take to upload and align their vocabularies with Wikidata (pro.europeana.eu/post/why-data-partners-should-link-their-vocabulary-to-wikidata-a-new-case-study, accessed on 10 May 2021). Wikidata has been widely employed, along with ontologies in two directions: (i) Wikidata as an enhancement for local metadata [27; 28; 29]; (ii) Wikidata as a knowledge hub, where domain knowledge can be injected so to enrich the general knowledge base [26; 29; 30; 31]. The direction closest to the approach described in this paper is the enhancement of local metadata on the basis of Wikidata. Recently, several efforts have been devoted to enrich local knowledge bases exploiting Wikidata. For example, [PERSON] and colleagues improved the access to a collection of Dutch historical newspapers by linking named entities mentioned in the news to corresponding Wikidata entries [32]. Once the first step of named entities recognition has been performed, the authors exploited machine learning to directly match entity mentions to Wikidata elements. [PERSON] relies on Wikidata to enhance the list of Holocaust-era ghettos [27]. The project is aimed at enriching the European Holocaust Research Infrastructure ghettos' authority by matching entries in the project dataset and categories in Wikidata through geographical information and names of ghettos. [PERSON] and colleagues tackle the problem of automatically augmenting ontologies in the framework of information extraction processes [28]. The authors start with a domain specific ontology, defined by domain experts, and enrich it on the basis of external knowledge bases, such as WordNet [33] and Wikidata. In order to enrich the ontology, the authors search Wikidata by using ontology class names and then match the results with concepts previously identified thanks to WordNet. More recently, [PERSON] and colleagues have emphasized the transition from a world made of multiple local identifiers to a world in which the unique knowledge base identifier is provided by Wikidata [34]. Unfortunately, none of the aforementioned works aims at building complex mappings between the involved semantic models as the ones addressed in this paper (Section 3), in which complex patterns are involved. **Ontology matching. ** Ontology matching [13] is the task of discovering and representing semantic correspondences among entities of different ontologies (usually two, but sometimes more). Such correspondences can be undirected or directed. In the latter case, they are usually called _mappings_. The need of ontology matching originates from the presence of different and overlapping ontologies in contexts where distinct pieces of information must be shared, integrated, translated in a common language or put in correspondence with each other. In many cases, this is a complex and expensive task; thus, its automatization is the subject of an active research field [35]. A semantic correspondence between ontology entities can be simple or complex [36]. In the former case, the correspondence expresses a relation between two simple entities, such as named classes, properties, or individuals; for instance, it may state that a named class in an ontology is equivalent to a named class in another ontology. In the latter case, the semantic correspondence refers to, at least, one complex expression, i.e., an expression composed of constructors or transformation functions applied to simple entities and/or to other complex expressions; it may state, for instance, that the set of individuals having some values for a property in an ontology is equivalent to the (set denoted by the) union of two classes in another ontology. The correspondences usually express equivalence, subsumption, disjointness, or some general relation.