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

Searching and organizing images across languages

With the continual growth of users on the Web from a wide range of countries, supporting such users in their search of cultural heritage collections will grow in importance. In the next few years, the growth areas of Internet users will come from the Indian sub-continent and China. Consequently, if holders of cultural heritage collections wish their content to be viewable by the full range of users coming to the Internet, the range of languages that they need to support will have to grow. This paper will present recent work conducted at the University of Sheffield (and now being implemented in BRICKS) on how to use automatic translation to provide search and organisation facilities for a historical image search engine. The system allows users to search for images in seven different languages, providing means for the user to examine translated image captions and browse retrieved images organised by categories written in their native language

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Visualising the South Yorkshire floods of ‘07

This paper describes initial work on developing an information system to gather, process and visualise various multimedia data sources related to the South Yorkshire (UK) floods of 2007. The work is part of the Memoir project which aims to investigate how technology can help people create and manage long-term personal memories. We are using maps to aggregate multimedia data and to stimulate remembering past events. The paper describes an initial prototype; challenges faced so far and planned future work

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