# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 2 Background and Related Work
### Semantic Web Technologies  
Ontologies play a key role in the Semantic Web, because they provide a technique for dealing with various and heterogeneous web resources and give machine-interpretable semantic vocabulary [12]. They were developed to facilitate knowledge sharing and reuse as they can be widely used to model domain information effectively due to their ability to represent and organize information, context, and relationships more accurately. Thus, ontologies are becoming increasingly popular as a result of their ability to provide a shared and common understanding of a domain between humans and application systems [13]. As such, the use of ontologies and supporting tools offers an opportunity to significantly improve knowledge management capabilities in many different fields. The Resource Description Framework (RDF) is a general knowledge representation for the web that aims to enable the encoding, exchange, and reuse of structured metadata [14]. RDF provides a model for representing knowledge as a set of triples, which consist of _subject-property-object_ statements [15]. Each triple represents a relationship between two resources, with the subject and object being resources (identified by URIs) and the property representing the relationship between them. A set of RDF statements is known as an RDF graph, which enables data integration, linking, and reasoning across different domains and applications [16]. RDF is complemented by other technologies and standards such as OWL (Web Ontology Language) for defining ontologies and the SPARQL query language for querying RDF data. OWL is a knowledge representation language commonly used to create ontologies in the Semantic Web community [17]. The syntax of OWL is the syntax of RDF, and the semantics of OWL are an extension of the semantics of RDF, according to the definition of OWL as an extension to RDF in the form of a vocabulary entailment [18]. Especially, OWL is a Semantic Web language for representing rich and complex information about things, groups of things, and the relationships between them. OWL contains three more expressive sublanguages developed for use by specific communities of users: OWL Lite, OWL DL, and OWL Full [19]. These sublanguages provide different levels of expressiveness and reasoning capabilities, allowing users to select the appropriate level of complexity based on their requirements. OWL DL is a notational variant of description logic (DL) [20], as OWL Lite uses a subset of the OWL language features and has more limitations on using the features than OWL DL or OWL Full. The most expressive OWL sublanguage is OWL. It is intended for usage in cases where a high level of expressiveness is more important than the language's decidability or computational completeness [19]. OWL ontologies can describe a wide range of knowledge, from simple taxonomies to complex domains with complicated relationships. They can be utilized in a variety of applications, including data integration, information retrieval, semantic search, intelligent agents, and more. To retrieveinformation based on the defined knowledge, OWL ontologies can also be queried using the SPARQL query language. SPARQL [21] is a declarative language proposed by the W3C for obtaining and updating information from RDF graphs. It is an expressive language that allows one to explain complex relationships between individuals. The semantics and complexity of the SPARQL query language have been thoroughly examined theoretically, revealing that SPARQL algebra is as expressive as relational algebra. SPARQL is best known as an RDF query language. Still, it can also be used to build SPARQL rules that can combine smaller RDF graphs into bigger ones to create new RDF data. The result is an RDF graph created by taking each query solution in the solution sequence, replacing the variables in the graph template and developing a combination to merge the triples into just one RDF graph.