## 2 Background
### Semantic Modeling in IoUT  
Semantic modeling concerns the conceptual modeling of domain knowledge in order to describe structured data (with formal semantics/metadata) in a specific logical way. Ontologies are formal vocabularies of concepts and relations, used for the semantic modeling and integration of heterogeneous data. An ontology is defined as the formal and explicit specification of conceptualizations which are used to assist programs and humans to share knowledge, describing entities and relationships among them [22]. Ontologies are versatile tools that provide the means for machines to understand the meaning of terms provided in natural language. They should be reusable in terms of their concepts, axioms, instances, and relationships. Furthermore, according to the NeOn (networked ontologies) [23] methodology, ontologies should offer interoperability, modularity, and extensibility. Formal ontology employs machine-readable languages such as the Resource Description Framework Schema or the Web Ontology Language (RDFS/OWL), in combination with other semantic tools such as query engines, knowledge management tools, and automated reasoners. Semantic reasoning is the ability of a machine to infer logical consequences from a set of asserted facts [24]. Ontologies can improve the interoperability of IoUT assets.  
A hybrid approach of context reasoning for underwater robots is proposed in [25], to cover the uncertainties of underwater environments. With the aim to expand the collaboration and cooperation of UUVs/AUVs, as well as the context-awareness concept, an ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning method is proposed. This framework is proposed to support the SWARM project and SWARM ontology [26], presenting a complete approach to context management and modeling of heterogeneous contexts using ontologies for underwater robots. Information fusion and reasoning techniques improve standardization, and provide a joint scheme of understandable information exchange, supporting cyber and trajectory situational awareness. Situational awareness (SA), which is a necessary condition for UUVs to be able to interoperate and to move safely, is represented in the ontology [27]. To achieve an autonomous decision-making loop for the \"leader\" of a swarm, it is critical that data can be handled effectively across various platforms and domains, and should be able to be reviewed, stored, accessed and shared efficiently. Almost as important as the mission and path planning is the adaptability of the mission and the recovery from failures [28]. The issue emerges when different protocols of communication are used, and due to the lack of standardization, interoperability is much more difficult to achieve. Semantic modeling of common communication protocols represented as ontologies is required to accomplish semantic interoperability between IoUT assets. It enables autonomous vehicles to understand the environmental situation, integrate new technologies by identifying them almost dynamically, perceive the reason of its actions and the purpose of its existence, and create the desirable autonomous decision-making loop.