# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 3 Materials and Methods
### Semantic Web Technologies  
Frequently known as the next-generation Web, the Semantic Web is intended to represent Web content in a well-defined manner, improving the ability of computers and humans to communicate and leverage the power of these representations. The basic idea behind this technology is to structure the content of Web pages by providing sufficient context about the resources available on the Web and the links that connect them as well as tools to automatically use this context to discover data and make decisions [53]. To represent Semantic Web knowledge, some technologies are used, mainly the resource description framework (RDF), which is a structured metadata representation that identifies Web-based resources by means of URIs; a graph model to describe resource relationships; RDF Schema for describing the classes and the properties of that resources in the RDFmodel; the Simple Knowledge Organization Systems (SKOS) for describing vocabularies and classifications; the SPARQL language for querying the RDF data; and also the Web Ontology Language (OWL) for defining ontologies and relationships between data [54].  
In the Semantic Web, an ontology can be defined as a more complex structure for providing explicit specifications of important domain resources, their types, properties, and hierarchies. There are some important and useful online ontologies that may be used freely to build enhanced applications able to identify relationships to discover hidden patterns, and DBpedia is one of the biggest [55]. DBpedia is a massive collection of semantic knowledge derived primarily from Wikipedia articles, with millions of entities defined and classified in a consistent ontology by distinct classes and properties. Currently, the DBpedia ontology contains 685 distinct classes, each of which is described by 2795 distinct properties and stored in RDF datasets that can be queried by users using well-known endpoint SPARQL queries to identify semantic relationships and properties. Additionally, these DBpedia properties are linked to other well-known linked data resources, such as WikiData and YAGO, which are used to further improve the query results [56].  
In fact, in order to identify relationships among texts, we must first identify the significant concepts in such texts to follow their properties in ontologies and establish the necessary connections, and one helpful tool for this is the DBpedia Spotlight (DB-SL). DB-SL analyzes raw text data to discover existing mentions of real DBpedia entities; that is, it translates simple text into structured data about specific DBpedia entities [57]. The user provides the text, and DB-SL examines it and finds some strings in it (known as surface form) with the goal of finding the associated mentions of existing DBpedia entities, which is a specific concept defined in the realm of the DBpedia dataset (referred usually to as named entity). It is important to note that, frequently, the same string can lead to different entities depending on the surrounding contents because DB-SL determines the appropriate association between surface forms and DBpedia entities based on the text context [58]. Queries to DB-SL can be configured using different parameter settings, as can the level of confidence in the detected mapping or some other constraints to be considered. A successful query response is composed of a list of selected surface forms together with the associated entities, as well as the similarity score computed between the identified surface form and the corresponding entity. An example of an entity description identified for the surface form \"Nile\" is provided in Table 1.  
We will use the DB-SL tool to annotate unstructured data extracted from various sources and convert them into relevant features for both users and provided images. The DBpedia ontology serves as the foundation for all of our data extension work, acting as an external database with all the knowledge required to find more classes related to the data extracted from different sources.