Self-supervised automated wrapper generation for weblog data extraction

Data extraction from the web is notoriously hard. Of the types of resources available on the web, weblogs are becoming increasingly important due to the continued growth of the blogosphere, but remain poorly explored. Past approaches to data extraction from weblogs have often involved manual intervention and suffer from low scalability. This paper proposes a fully automated information extraction methodology based on the use of web feeds and processing of HTML. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a dataset of 2,393 posts and the results (92% accuracy) show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere for applications such as improved information retrieval and more robust web preservation initiatives

Design of Automatically Adaptable Web Wrappers

Nowadays, the huge amount of information distributed through the Web motivates studying techniques to\ud be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises\ud developed several approaches of Web data extraction, for example using techniques of artificial intelligence or\ud machine learning. Some commonly adopted procedures, namely wrappers, ensure a high degree of precision\ud of information extracted from Web pages, and, at the same time, have to prove robustness in order not to\ud compromise quality and reliability of data themselves.\ud In this paper we focus on some experimental aspects related to the robustness of the data extraction process\ud and the possibility of automatically adapting wrappers. We discuss the implementation of algorithms for\ud finding similarities between two different version of a Web page, in order to handle modifications, avoiding\ud the failure of data extraction tasks and ensuring reliability of information extracted. Our purpose is to evaluate\ud performances, advantages and draw-backs of our novel system of automatic wrapper adaptation

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Automatic Wrapper Adaptation by Tree Edit Distance Matching

Information distributed through the Web keeps growing faster day by day,\ud and for this reason, several techniques for extracting Web data have been suggested\ud during last years. Often, extraction tasks are performed through so called wrappers,\ud procedures extracting information from Web pages, e.g. implementing logic-based\ud techniques. Many fields of application today require a strong degree of robustness\ud of wrappers, in order not to compromise assets of information or reliability of data\ud extracted.\ud Unfortunately, wrappers may fail in the task of extracting data from a Web page, if\ud its structure changes, sometimes even slightly, thus requiring the exploiting of new\ud techniques to be automatically held so as to adapt the wrapper to the new structure\ud of the page, in case of failure. In this work we present a novel approach of automatic wrapper adaptation based on the measurement of similarity of trees through\ud improved tree edit distance matching techniques