Towards completely automatized HTML form discovery on the web - Moraes, Maurício Coutinho (2013)
The discovery of HTML forms is one of the main challenges in Deep Web crawling. Automatic solutions for this problem perform two main tasks. The first is locating HTML forms on the Web, which is done through the use of traditional/focused crawlers. The second is identifying which of these forms are indeed meant for querying, which also typically involves determining a domain for the underlying data source (and thus for the form as well). This problem has attracted a great deal of interest, resulting in a long list of algorithms and techniques. Some methods submit requests through the forms and then analyze the data retrieved in response, typically requiring a great deal of knowledge about the domain as well as semantic processing. Others do not employ form submission, to avoid such difficulties, although some techniques rely to some extent on semantics and domain knowledge. We offer an up-to-date review of 19 methods for the discovery of domain-specific query forms that do not involve form submission. This thesis details these methods and discusses how form discovery has become increasingly more automated over time, providing the context in which we propose a novel method to advance the current state-of-the-art in domain-specific structured HTML form discovery. The current state-ofthe- art in domain-specific structured HTML form discovery consists mainly of methods that directly or indirectly depend heavily on human intervention. This thesis proposes and evaluates a method capable of discovering domain-specific structured HTML forms on the Web with very little effort from a human expert, who is required only to define the name of the domain of interest (i.e., the domain for which the discovery should be made). The forms discovered by our proposal can be directly used as training data by some form classifiers. Our experimental validation used thousands of real Web forms, divided into six domains, including a representative subset of the publicly available DeepPeep form base (DEEPPEEP, 2010; DEEPPEEP REPOSITORY, 2011). Our results show that it is feasible to mitigate the demanding manual work required by two cutting-edge form classifiers (i.e., GFC and DSFC (BARBOSA; FREIRE, 2007a)), at the cost of a relatively small loss in effectiveness.