# Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives
## 6 Related Work
Furthermore, it is worth noting that HERO offers some basic ontological notions that are needed to account for the considered domain, and are useful for reasoning about it; however, these are not usually present in cross-domain ontologies and are left as implicit in many datasets, including Wikidata. For example, this is the case of social roles and their relations [9], of the notions of role institutionalization by organizations, and of the affiliation of agents to organizations [10]. Many complex mappings that we defined do rely on these notions, and thus, they actually include an inference step that makes explicit some knowledge that is not even mentioned in Wikidata (either by the data or by the underlying model). This would be a further obstacle to the exploitation of any available ontology matching system as well as an interesting but hard challenge to automatic complex matching research. Type (iii) mappings were manually specified (Section 3.1). Our automatic matching system (Section 3.1) performed poorly on properties. It is known that in some cases, ontology matchers have some problems with properties [41]. In our case, we conjecture that this phenomenon could be caused by some peculiarities of the HERO model, where some properties with specific meaning have labels containing common names, which are possibly misleading for our matching system. For instance, the HERO property _hasEndingEvent_ (labeled as \"ending event\", \"has as the ending event\", \"evento finale\", \"ha come evento finale\"), whose intended meaning is a relation between perdurants (events or states) and events representing their conclusion (e.g., the end of World War II), has been wrongly stated as equivalent to the Wikidata property _final event_ (P3967), which actually represents the final event of a competition. However, an in-depth analysis of this phenomenon is out of the scope of the present work, and we leave it as a future work. For what concerns types (i) and (ii) mappings, we firstly automatically computed a set of candidate mappings, which we manually evaluated and completed. The automatic step was based on a language-based element-level matching system [13] that makes use of external resources (Section 3.1). The decision of implementing a simple matcher tailored to our needs instead of using any available matching systems is motivated by several reasons. Firstly, current state-of-the art matching systems still have problems in matching a domain ontology with a cross-domain one [41], which is exactly our case. Secondly, the different approaches by which HERO and Wikidata were built entail some significant ontological differences between the two semantic models. HERO is based on an accurate ontological analysis, which exploits both existing foundational and core ontologies and the domain experts' knowledge, which was aimed at providing a well-founded and sound framework for domain-specific instance data [4; 42]. Differently, Wikidata [7] is a large-scale crowd-based project aimed at collecting and providing users with a free huge amount of data in any domain. In Wikidata, the community can contribute both to instance data and to the schema (i.e., to the underlying ontology). Conflicting data are explicitly admitted, and some kind of vagueness or any idiosyncratic position can be reasonably expected also at the ontological level. For instance, let us consider the Wikidata _part of_ (P361) transitive property. Wikidata states that [PERSON] was _part of_ the 109 th United States Congress (www.wikidata.org/wiki/Q76, accessed on 10 May 2021). If we add the statement that [PERSON]'s brain is part of [PERSON] (which would be a valid usage of the P361 property), we could derive that the brain of [PERSON] was part of the 109 th United States Congress. This paradoxical conclusion results from the lack of distinction in Wikidata between the different notions of part-whole relations [43].