# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 3 Overview of the SWARMs Context-Aware Framework for Underwater Robots
### The Context-Aware Framework
All data can be displayed with a homogeneous view associated with the semantic content. The ontology model could annotate context uncertainties based on MEBN theories. Specifically, probability information about uncertain context can be modeled using ontology constructs defined in the PR-OWL [39] ontology. With PR-OWL classes and properties, ontology engineers could fully specify an MEBN model while maintaining compatibility with the OWL ontology language. The SWARMs ontology will be briefly introduced in Section 3.2. * _Semantic Mapper_. This component plays a vital role in facilitating the transparent sharing of information. As data might be formatted in different manners pertaining to different data sources, this component aims to parse and formalize them in an ontology-compliant format. Translations from different standards, such as XML, JSON, or binary files, to ontological formations, such as Resource Description Framework (RDF) [40] or OWL, can be enabled in this component. For differently formatted data that do not comply with the SWARMs ontology, corresponding mapping files should be predefined in order to parse and map them into the common information model. * _Rules Creator_. Operators or marine experts are able to define rules based on their knowledge before or during missions through this component. A set of user-defined rules can be translated into the SWRL format and inserted into the ontology model. Rules can be diverse, including restrictions or definitions for entities, regulations for evaluating data values, or specifications for relationships between entities. The rule set is very important for the context reasoner to consider when making inferences. * _Context Reasoner_. The essential capability of this component is to derive new knowledge from available contexts stored in the ontology model. Basically, it will consult information stored in the ontology model and also take experiences and knowledge from marine experts into account. A hybrid reasoning mechanism, including the ontological, rule-based, and MEBN reasoning, is intended to be employed in this context reasoner. Specifically, the ontological reasoning enables several kinds of operations, including concept satisfiability, consistency check, class subsumption, and logic inference. The rule-based reasoning could augment the ontological reasoning in terms of logicality and human readability. The MEBN reasoning is dedicated to reasoning under uncertainties. * _Semantic Query_. This component deals with any semantic query made by operators. It receives queries, calls corresponding reasoning services, and finally outputs answers. This is one of the context dissemination strategies defined in the SWARMs project. Apart from this, the SWARMs project also follows a subscribe/publish paradigm to distribute context information. The context-aware framework provides architectural support for different context treatments. Within the framework, the context reasoner is significant as it is able to provide the necessary services to meet all the reasoning requirements in the SWARMs project. In particular, it can handle the intricacies inherent to the reasoning over uncertainties. The hybrid context reasoning, proposed for the context reasoner component, will be presented in Section 4.