## 2 Literature Review
### Ontologies for Regulatory Technology  
Over the past few decades, semantic technology based on ontologies has been extensively utilized in information integration and conceptual modeling applications [5]. A commonly cited brief definition of an ontology states that it is a specification of a conceptualization [16]. A more comprehensive definition of the term has been given by [PERSON] (1998) [17], where an ontology is \"_a logical theory accounting for the intended meaning of a formal vocabulary, i.e., its ontological commitment to a particular conceptualization of the world. The intended models of a logical language using such a vocabulary are constrained by its ontological commitment. An ontology indirectly reflects this commitment (and the underlying conceptualization) by approximating these intended models.\"_  
Ontologies support the development of models built on a common vocabulary, thus providing a unification-based umbrella for the conceptual representation of all the relevant data in a particular domain, which is stored in various formats and under different labels in often dispersed data storages. The main building blocks of an ontology are classes, which represent concepts having entities with common characteristics. Classes are linked with relations, which define how classes are related to each other. A subclass is one such relation, which inherits the characteristics of the parent class to its child, thus allowing the formation of class hierarchies, very much like the object-oriented paradigm in software engineering. Individuals of a class (i.e., class objects) are the instances belonging to that particular class. Object properties are binary relations between individuals, while data properties are binary relations between an individual and data values. Finally, axioms are constraints on the class level, which enforce restrictions on the class individuals. Ontologies reach their full potential when combined with formal logic. Such an approach makes them formal ontologies, which according to [PERSON] et al. (2011) [PERSON] et al. (2011), \"_require a clear semantics for the language used to define the concept, clear motivations for the adopted distinctions between concepts as well as strict rules about how to define concepts and relationships_\". This is obtained by using an appropriate formal logic (usually first order logics or description logics), where the meaning of the concept is guaranteed by formal semantics. Formal ontologies can provide the foundations on which information systems managing knowledge can be built. Furthermore, this knowledge can be processed by a reasoner to infer new knowledge and support decision making. Since ontologies augment common understanding between users, domain knowledge managed or inferred with the use of a formal ontology must be consensual, in order that it can be shared and reused across different software systems and by various types of users ([PERSON] et al., 2011). Data in the pharmaceutical manufacturing industry are massive, heterogeneous, and complex and hard to effectively process. Moreover, the knowledge inference required by a regulatory technology (RegTech) software for regulatory compliance monitoring, has become a very challenging task. For all these reasons, ontology technology has been adopted in the context of the SPuMoNI project, with particular focus on comprehensively offering:  
* _Semantic data representation_. Ontologies can inherently support pharma product and process development due to both their openness and richness of semantics for representing, analyzing, interpreting, and managing large amounts of complex and diverse information and their independence from any software or data storage platform. Furthermore, the utilization of ontology features, such as properties, axioms, and rules, can enable the enhancement of data consistency and also can facilitate reasoning based on semantics;
* _Fast and compact decisions during product and process development_.