## 2 Related Work  
This section presents relevant SFWC approaches proposed in the literature and the most recurrent metrics to measure the web page similarity in a given domain ontology. SFWC approaches [22; 24; 25] exploit the expressiveness of an ontology to compute the similarity of a web page content against a domain ontology. Table 1 summarizes some ontology-based SFWC targeting different tasks, describing the measure used to determine the web page relevance. As it is shown, the cosine similarity is the most common measure used to determine the relevance of a web page against an ontology content. SFWCs could be applied to different domains such as recommendation systems [25; 26; 27] or cybercrime [28], as Table 1 shows. In all cases, a specific domain ontology must define the most relevant elements and their relationship in the given domain. These approaches leave aside the semantic analysis of the source content, which could be exploited to better discrimination of web resources related to the domain. The proposed SFWC tries to alleviate the aforementioned situation, providing a semantic analysis to represent the relationship between content (words) and source (documents) through the KRS. The proposed KRS defines a set of classes with certain properties based on the SW standard RDFS. The KRS is a lightweight version of an ontology since it does not define complex elements like axioms or formal constrains but it is also based on SW technologies. The KRS depends on the input corpus to model a topic, i.e., the content information of the corpus is used to generate the KRS. The schema provides an incremental feature, i.e., the KRS could be expanded with more specific domain documents since entities are independent between them but related by the source, e.g., all words from the same document are linked together. Traditional SFWCs are based on metrics like semantic relevance or cosine similarity to determine the relevance of a web page to a given domain. This kind of metric is used to measure the distance between two elements in an ontology. TF-IDF is a metric that has been used by different approaches to characterize a corpus and built a classification model [30; 31; 32]. [PERSON] et al. [30] present a Naive Bayes classifier based on TF-IDF to extract the features of a web page content. [PERSON] et al. [31] propose a new measure called Term Frequency-Information Content as an improvement of TF-IDF to crawl multi-term topics. A multi-term topic is a compound set of keywords that could not be eliminated to kept the meaning of the whole topic, e.g., web services. [PERSON] et al. [32] present a partition algorithm to segment a web page into content blocks. TF-IDF was used also to measure the relevance of content blocks and to build a vector space-model [33] to retrieve topic and genre-related web pages. [PERSON] and [PERSON] [34] proposed a Term-Frequency Inverse-Document Frequency Definition Semantic (TIDS). TIDS is a table of words associated with the sum of all TF-IDF values in the corpus. [PERSON] et al. [35] proposed the combination of TF-IDF with LSI (Latent Semantic Indexing) to improve crawling results. TF-IDF has been also used as a feature space to built structures in tasks like machine learning, multi-term topic, content block analysis, and table indexing to create complex models to determine the similarity between a document and a target domain. For example, a classification model based on TF-IDF requires a test and training set to generate a model, and the addition of more documents could lead to generate a new model based on a new test and training set and to compute TF-IDF values again. If a new document is added to the corpus, an FWC based on TF-IDF needs to compute again this value over all document's words in the corpus. In this work, we proposed the use of IDF as similarity measure since it provides the importance of a word in the corpus.