1. The Ontological Background

«The Semantic Web can be seen as an evolution of the WWW in which machines can “understand” the meaning of the information and services available on it. This goal is enabled by the usage of languages and technologies that support a description of Web resources in terms of concepts and relations they refer to» (Goy and Magro 2015, p. 7463).

Goy and Magro (2015) emphasize such a possibility asserting that, first of all, ontologies can support communication between human beings, between human beings and software systems, and between software systems themselves. In the former case, ontologies are informal, or semi‐formal, representations, that are sometimes used by companies to enable concepts and “standard” terminology sharing, within a single company, or between companies. In the case of communication between human beings and software systems, ontologies can be useful to enhance information access. Interesting examples of this use are the so‐called “semantic portals”. Usually, ontologies enable these systems to link contents to concepts and relationships explicitly represented within the system itself, thus enabling the aggregation and integration of such contents. In the case of communication among software systems, the importance of an explicit use of semantic representations has been recognized also in the field of information search on the Web (or in large information repositories), in order to improve search service performance (“semantic search”). This means building search engines with user interfaces presenting the underlying “semantic knowledge” to users, thus enabling her/him to express, refine, expand her/his query on the basis of this knowledge. The communication between software applications usually consists in exchanging messages. In this sense, the applications must share, at least, the encoding format, the information representation syntax, and some communication protocols. However, in order to “understand” the information produced by another application, a shared reference conceptualization is needed. In many cases, this level is not explicitly represented, but it is hardcoded within the application code. Moreover, ontologies could also be used to achieve (at least a partial) semantic interoperability, by sharing the same ontologies, by translating internal representations of the exchanged contents in terms of a shared ontology, by explicitly specifying a mapping between the different ontologies adopted by the involved applications or between them and a shared ontology. Finally, ontologies expressed in logical languages enable automatic inferences, aiming at making explicit knowledge that is implicit, either in ontologies or in the datasets characterized in terms of them.

The term “reality” is used in a broad sense, which includes, for example, physical entities, counterfactual ones, imaginary entities, and so forth (Goy and Magro 2015, p. 7457).

Other lists of definitions of ontology in computer science can be found in Gómez-Pérez et al. (2004), Bullinger (2008), Jaziri and Gargouri (2010).

Cfr. Uschold (1996), Fensel (2001), Zelewski et al. (2001), Mizoguchi (2003).

Cfr. Borst (1997), Guarino (1998), Studer et al. (1998), Uschold (1998), Fikes et al. (1999), Sowa (2000).

Cfr. Studer et al. (1998).

Cfr. Guarino and Giaretta (1995), Uschold and Grueninger (1996), Guarino (1998), Goy and Magro (2015).

Cfr. Zelewski et al. (2001), Hesse (2002), Mizoguchi (2003), Krcmar (2005).

See in particular Martin and Heil (1999).

Cfr. Strawson (1959), Jackson (1998).

Cfr. Lewis (1986).

Cfr. Armstrong (1978, 1983, 1989, 1997).

Cfr. Körner (1974, 1984), Lowe (1989, 1998, 2006, 2009).

Cfr. Mulligan (2000), Simons (1987, 1994), Smith (1982, 1996, 1997).

Cfr. Chisholm (1976, 1984, 1996).

Cfr. Bergmann (1967), Grossmann (1992), Tegtmeier (1992).

Cfr. Ferraris (2008: pp. 16–7), Bianchi and Bottani (2003).

Cfr. Berto (2010).

Cfr. Ferraris (2008).

According to these two aspects, the ontology of geography might also be defined as that discipline which studies in particular: geographic entities (entities such as mountains, oceans, countries, etc.), their borders (natural and/or artificial, regardless of the fact that these boundaries might be part of the entities they define), their spatial representation (in maps, software, etc.), their mereological and topological relations, and their location.

This paragraph is taken from Tambassi 2016a, pp. 54–55.

Cfr. Horton (1982).

This paragraph is taken from Tambassi 2016a, p. 55.