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

The DIGMAP geo-temporal web gazetteer service

This paper presents the DIGMAP geo-temporal Web gazetteer service, a system providing access to names of places, historical periods, and associated geo-temporal information. Within the DIGMAP project, this gazetteer serves as the unified repository of geographic and temporal information, assisting in the recognition and disambiguation of geo-temporal expressions over text, as well as in resource searching and indexing. We describe the data integration methodology, the handling of temporal information and some of the applications that use the gazetteer. Initial evaluation results show that the proposed system can adequately support several tasks related to geo-temporal information extraction and retrieval

Development of Use Cases, Part I

For determining requirements and constructs appropriate for a Web query language, or in fact any language, use cases are of essence. The W3C has published two sets of use cases for XML and RDF query languages. In this article, solutions for these use cases are presented using Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web data such as XML documents with access to Semantic Web metadata such as RDF resource descriptions with reasoning abilities and rules familiar from logicprogramming. To the best knowledge of the authors, this is the first in depth study of how to solve use cases for accessing XML and RDF in a single language: Integrated access to data and metadata has been recognized by industry and academia as one of the key challenges in data processing for the next decade. This article is a contribution towards addressing this challenge by demonstrating along practical and recognized use cases the usefulness of reasoning abilities, rules, and semistructured query languages for accessing both data (XML) and metadata (RDF)

The XML Query Language Xcerpt: Design Principles, Examples, and Semantics

Most query and transformation languages developed since the mid 90es for XML and semistructured data—e.g. XQuery [1], the precursors of XQuery [2], and XSLT [3]—build upon a path-oriented node selection: A node in a data item is specified in terms of a root-to-node path in the manner of the file selection languages of operating systems. Constructs inspired from the regular expression constructs , +, ?, and “wildcards” give rise to a flexible node retrieval from incompletely specified data items. This paper further introduces into Xcerpt, a query and transformation language further developing an alternative approach to querying XML and semistructured data first introduced with the language UnQL [4]. A metaphor for this approach views queries as patterns, answers as data items matching the queries. Formally, an answer to a query is defined as a simulation [5] of an instance of the query in a data item