A Novel Web Scraping Approach Using the Additional Information Obtained from Web Pages

Web scraping is a process of extracting valuable and interesting text information from web pages. Most of the current studies targeting this task are mostly about automated web data extraction. In the extraction process, these studies first create a DOM tree and then access the necessary data through this tree. The construction process of this tree increases the time cost depending on the data structure of the DOM Tree. In the current web scraping literature, it is observed that time efficiency is ignored. This study proposes a novel approach, namely UzunExt, which extracts content quickly using the string methods and additional information without creating a DOM Tree. The string methods consist of the following consecutive steps: searching for a given pattern, then calculating the number of closing HTML elements for this pattern, and finally extracting content for the pattern. In the crawling process, our approach collects the additional information, including the starting position for enhancing the searching process, the number of inner tag for improving the extraction process, and tag repetition for terminating the extraction process. The string methods of this novel approach are about 60 times faster than extracting with the DOM-based method. Moreover, using these additional information improves extraction time by 2.35 times compared to using only the string methods. Furthermore, this approach can easily be adapted to other DOM-based studies/parsers in this task to enhance their time efficiencies. © 2013 IEEE

Declarative approach to data extraction of web pages

Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfilment of the requirements for the degree of Master in Computer ScienceIn the last few years, we have been witnessing a noticeable WEB evolution with the introduction of significant improvements at technological level, such as the emergence of XHTML, CSS,Javascript, and Web2.0, just to name ones. This, combined with other factors such as physical expansion of the Web, as well as its low cost, have been the great motivator for the organizations and the general public to join, with a consequent growth in the number of users and thus influencing the volume of the largest global data repository. In consequence, there was an increasing need for regular data acquisition from the WEB, and because of its frequency, length or complexity, it would only be viable to obtain through automatic extractors. However, two main difficulties are inherent to automatic extractors. First, much of the Web's information is presented in visual formats mainly directed for human reading. Secondly, the introduction of dynamic webpages, which are brought together in local memory from different sources, causing some pages not to have a source file. Therefore, this thesis proposes a new and more modern extractor, capable of supporting the Web evolution, as well as being generic, so as to be able to be used in any situation, and capable of being extended and easily adaptable to a more particular use. This project is an extension of an earlier one which had the capability of extractions on semi-structured text files. However it evolved to a modular extraction system capable of extracting data from webpages, semi-structured text files and be expanded to support other data source types. It also contains a more complete and generic validation system and a new data delivery system capable of performing the earlier deliveries as well as new generic ones. A graphical editor was also developed to support the extraction system features and to allow a domain expert without computer knowledge to create extractions with only a few simple and intuitive interactions on the rendered webpage

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

AutoDesc: Facilitating Convenient Perusal of Web Data Items for Blind Users

Web data items such as shopping products, classifieds, and job listings are indispensable components of most e-commerce websites. The information on the data items are typically distributed over two or more webpages, e.g., a ‘Query-Results’ page showing the summaries of the items, and ‘Details’ pages containing full information about the items. While this organization of data mitigates information overload and visual cluttering for sighted users, it however increases the interaction overhead and effort for blind users, as back-and-forth navigation between webpages using screen reader assistive technology is tedious and cumbersome. Existing usability-enhancing solutions are unable to provide adequate support in this regard as they predominantly focus on enabling efficient content access within a single webpage, and as such are not tailored for content distributed across multiple webpages. As an initial step towards addressing this issue, we developed AutoDesc, a browser extension that leverages a custom extraction model to automatically detect and pull out additional item descriptions from the ‘details’ pages, and then proactively inject the extracted information into the ‘Query-Results’ page, thereby reducing the amount of back-and-forth screen reader navigation between the two webpages. In a study with 16 blind users, we observed that within the same time duration, the participants were able to peruse significantly more data items on average with AutoDesc, compared to that with their preferred screen readers as well as with a state-of-the-art solution

Bootstrapping Web Archive Collections From Micro-Collections in Social Media

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. We investigate the problem of generating seed URIs automatically, and explore the state of the art in collection building and seed selection. Attempts toward generating seeds automatically have mostly relied on scraping Web or social media Search Engine Result Pages (SERPs). In this work, we introduce a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share narratives about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts Micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create Micro-collections is an indication of editorial activity and a demonstration of domain expertise. Therefore, we propose a model for generating seeds from Micro-collections. We begin by introducing a simple vocabulary, called post class for describing social media posts across different platforms, and extract seeds from the Micro-collections post class. We further propose Quality Proxies for seeds by extending the idea of collection comparison to evaluation, and present our Micro-collection/Quality Proxy (MCQP) framework for bootstrapping Web archive collections from Micro-collections in social media

A Genre-based Clustering Approach to Content Extraction

The content of a webpage is usually contained within a small body of text and images, or perhaps several articles on the same page; however, the content may be lost in the clutter (defined as cosmetic features such as animations, menus, sidebars, obtrusive banners). Automatic content extraction has many applications, including browsing on small cell phone and PDA screens, speech rendering for the visually impaired, and reducing noise for information retrieval systems. We have developed a framework, Crunch, which employs various heuristics for content extraction in the form of filters applied to the webpage's DOM tree; the filters aim to prune or transform the clutter, leaving only the content. Crunch allows users to tune what we call 'settings', consisting of thresholds for applying a particular filter and/or for toggling a filter on/off, because the HTML components that characterize clutter can vary significantly from website to website. However, we have found that the same settings tend to work well across different websites of the same genre, e.g., news or shopping, since the designers often employ similar page layouts. In particular, Crunch could obtain the settings for a previously unknown website by automatically classifying it as sufficiently similar to a cluster of known websites with previously adjusted settings. We present our approach to clustering a large corpus of websites into genres, using their pre-extraction textual material augmented by the snippets generated by searching for the website's domain name in web search engines. Including these snippets increases the frequency of function words needed for clustering. We use existing Manhattan distance measure and hierarchical clustering techniques, with some modifications, to pre-classify the corpus into genres offline. Our method does not require prior knowledge of the set of genres that websites fit into, but to be useful a priori settings must be available for some member of each cluster or a nearby cluster (otherwise defaults are used). Crunch classifies newly encountered websites online in linear-time, and then applies the corresponding filter settings, with no noticeable delay added by our content-extracting web proxy