Name: Freddy Brasileiro Silva
Type: MSc dissertation
Publication date: 28/09/2016
Advisor:

Namesort descending Role
João Paulo Andrade Almeida Advisor *

Examining board:

Namesort descending Role
Fernando Silva Parreiras External Examiner *
João Paulo Andrade Almeida Advisor *
Vítor Estêvão Silva Souza Internal Examiner *

Summary: Often, subject domains are conceptualized with entities in two levels: a level of classes, and a level of individuals which instantiate these classes. In several subject domains, however, classes themselves may be subject to categorization, resulting in classes of classes (or metaclasses). To represent these domains, one needs to capture not only entities of different classification levels, but also their (possibly intricate) relations. In the domain of biological taxonomies, for instance, a given organism (e.g. Cecil, the lion killed in
the Hwange National Park in Zimbabwe in 2015) is classified into taxa (such as, e.g., Animal, Mammal, Carnivoran, Lion), each of which is classified by a biological taxonomic rank (e.g., Kingdom, Class, Order, Species). Thus, to represent the knowledge underlying this domain, one needs to represent entities at different (but nonetheless related) classification levels. For example, Cecil is an instance of Lion, since he exhibits those common features.
For example, Cecil is an instance of Lion, which is an instance of Species. Species, in its turn, is an instance of Taxonomic Rank. Moreover, when representing these domains, one needs to capture not only entities of different classification levels, but also their (possibly intricate) relations. For example, we would like to state that instances of the genus Panthera must also be instances of exactly one instance of Species (e.g. Lion). The need to support the representation of knowledge domains dealing with multiple classification
levels has given rise to an area of investigation called multi-level modeling. We observe that the representation of multi-level domains is challenging in current Semantic Web languages, as there is little support to guide the modeler in producing correct multi-level ontologies, especially because of the nuances in the constraints that apply to entities of different classification levels and their relations. In order to address these representation challenges, we define a vocabulary that can be used as basis for the definition of multilevel
ontologies in OWL. This vocabulary is accompanied by integrity constraints to
prevent the construction of inconsistent models as well as derivation rules to derive knowledge that is not explicit in the model. We offer a tool that receives as input a domain model, checks its conformance with the proposed integrity constraints and produces an output model containing the original domain model plus derived information. In this process, we employ an axiomatic theory called MLT (a Multi-Level Modeling Theory). We use Wikidata content to demonstrate that the approach can prevent the construction of inconsistent multi-level representations in a realistic setting.

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