## 3 Methodology
### The KRS Construction  
The SW provides a set of standards to describe data and their metadata. The resource description framework (RDF) is the SW standard to describe structured data. The basic element of RDF is known as triple. A triple is composed of a subject, object, and a predicate to define the relationship between them. A set of related triples are known as RDF graph. The SW also provides additional standards to \begin{table}
\begin{tabular}{l l l} \hline \hline
**Task** & **Description** & **Measure** \\ \hline Cloud service & A concept ontology-based recommendation system for retrieving & Semantic relevance \\ recommendation system [25; 26] & cloud services. & \\ \hline Website models [29] & An ontology-supported website model to improve search engine results. & Cosine similarity \\ \hline Web directory construction [20] & Based on a handmade ontology from WordNet to automatically construct a web directory. & Cosine similarity \\ \hline User-based & A knowledge representation model built from a user interest database to & Concept similarity \\ recommendation system [27] & select seeds URLs. & \\ \hline Concept labeling [5; 22] & An ontology-based classification model to label new concepts during the & Cosine similarity, \\  & crawling process, integrating new concepts and relations to the ontology. & semantic relevance \\ \hline Cybercrime [28] & Enhanced crime ontology using ant-miner focused crawler. & Significance \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative ontology-based SFWCs. define more complex structures like ontologies, being RDFS and the Ontology Web Language (OWL) the standards for this purpose. These standards define the rules to build ontologies; however, RDFS is less expressive than OWL since it does not define restrictions or rules over the ontology. The KRS (Figure 2) is a general and domain-free structure to describe the entities from a text source. In this work, a corpus is represented as a set of KRS stored in a KB. The goal of the KB is to provide the mechanisms to query the content of the KRS to measure the similarity between a web page content and the KRS. The KRS is based on RDF and RDFS to define topic entities and relationships. It is built considering the well known NIF (The NLP Interchange Format) [36] vocabulary which provides interoperability between language resources and annotations. In the KRS, a word is an instance of _sfvc:Entity_, representing a word as a phrase (_nif:Phrase_) or as a single word (_nif:Word_). Each word is described considering the following elements: (i) lemma word (_nif:anchorOf_), (ii) NE tag (_sfvc:neTag_), (iii) Part of Speech Tagging (PoS Tag) (_nif:posTag_), (iv) url (_nif:taldentRef_) and (v) IDF (_sfvc:idValue_). These elements are used to determine if a new document is related to the target topic. A document instance (_sfvc:Document_) is described only by the title of the source document, and it is related to the target topic (_sfvc:Topic_). The steps followed to populate the KRS are the following (Figure 3):  
* A. Text processing
* Preprocessing: The content of each document is processed for subsequent analysis. At this stage, stop words are removed and words are labeled with its corresponding PoS Tag and lemma. * Noun enrichment (NNE): The enrichment process assigns to each noun a PoS Tag, lemma, NE label, semantic annotation, and their IDF value. The PoS Tag and lemma were extracted in the previous step. The NE label and the semantic annotation are identified by Named Entity Recognition (NER) and Named Entity Linking (NEL) algorithms over the text. NER identifies NEs from an input text and NEL disambiguates those NEs to a KB to assign  
Figure 2: The Knowledge Representation Schema to describe the relationship between a noun and a corpus. a unique identifier (URL). The IDF measure assigns a weight to each noun in accordance with their importance in the corpus. * B. Mapping process
* RDF triple representation: The enriched nouns information is used to populate the KRS.