## 1 Introduction  
According to the website Live Stats [1], there are more than one billion of active websites on the World Wide Web (WWW). As a result, the increasing necessity of faster and reliable tools to effectively search and retrieve web pages from a particular domain has been gaining importance. One of the most popular tools to systematically collect web pages from the WWW are web crawlers. A web crawler is a system based on Uniform Resource Locator (URL) indexing to traverse the Web. URLs indexing provides a better service to web search engines and similar applications to retrieve resources from the web [2]. The web crawler searches for any URL reachable from the web page being retrieved by the search engine. Each URL found by the crawler is placed in a search queue to later be accessed by the search engine. The process repeats for each new URL retrieved from the queue. The stop criterion for URL searching varies; the most common is until reaching a threshold in the number of URLs retrieved from a seed or when reaching a level of depth. The architecture of a web crawler is composed of three main components: (i) a URL frontier, (ii) the page downloader, and (iii) a repository. The Frontier stores the URLs that the web crawler has to visit. The page downloader retrieves and parses the web pages from the URLs in the frontier. Finally, the downloaded web pages are stored in the repository component [3]. From the huge amount of resources in the web, most of them could be irrelevant to the domain of interest. This is why focused web crawlers (FWC) are better preferred to retrieve web pages. An FWC is based on techniques such as machine learning (classification) to identify relevant web pages, adding them to a local database [4]. An FWC (Figure 1) adds to the traditional crawler architecture a topic classifier module. This module is featured-based, modeling an input target domain to classify relevant web pages. If the web page is positively classified, its URLs are extracted and queued in the frontier module. In some FWC approaches [5; 6; 7; 8], the classification module is based on document similarity metrics to filter related and non-related web pages to a given domain. However, these approaches do not take into account the expressiveness of web pages content, that is, they do not explore their semantic content or use that information in the filtering process. An FWC retrieves a set of topic-related web pages from a set of seed URLs. A seed URL is the starting point to iteratively extract URLs. That is, an FWC analyzes the content of seed URLs to determine the relevance of their content for a target domain. Such content analysis is based on techniques like ontology-based, machine learning, query expansion, among others [9]. Some approaches require an initial dataset to create a model (machine learning approaches [10]) or a set of keywords to produced specific domain queries (query expansion [11]). The Semantic Web (SW), considered as an extension of today's Web, is based on a resource description framework (RDF) to express information in a well-defined meaning [12]. The SW arranges data as a logically linked data set instead of a traditional hyperlinked Web. An FWC that exploits the semantics of the Web content and uses some ontology heuristics is called Semantic Focused Web Crawler (SFWC). An ontology is a specification of a conceptualization, describing the concepts and relationships that can exist between domain's elements [13]. An SFWC determines the relevance of a web page to a user's query based on domain knowledge related to the search topic [14]. An SFWC performs two main tasks [15]: (i) content analysis and (ii) URL traversing (crawling). The content analysis task consists of determining if a web page is relevant or not for the topic given by the user's query. Algorithms such as PageRank [16], SiteRank [17], Visual-based page segmentation (VIPS) [18], and densometric segmentation [19] are well known web page content analyzers.