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

Web based knowledge extraction and consolidation for automatic ontology instantiation

The Web is probably the largest and richest information repository available today. Search engines are the common access routes to this valuable source. However, the role of these search engines is often limited to the retrieval of lists of potentially relevant documents. The burden of analysing the returned documents and identifying the knowledge of interest is therefore left to the user. The Artequakt system aims to deploy natural language tools to automatically ex-tract and consolidate knowledge from web documents and instantiate a given ontology, which dictates the type and form of knowledge to extract. Artequakt focuses on the domain of artists, and uses the harvested knowledge to gen-erate tailored biographies. This paper describes the latest developments of the system and discusses the problem of knowledge consolidation

An Infrastructure for acquiring high quality semantic metadata

Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a erification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation omparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata

An infrastructure for building semantic web portals

In this paper, we present our KMi semantic web portal infrastructure, which supports two important tasks of semantic web portals, namely metadata extraction and data querying. Central to our infrastructure are three components: i) an automated metadata extraction tool, ASDI, which supports the extraction of high quality metadata from heterogeneous sources, ii) an ontology-driven question answering tool, AquaLog, which makes use of the domain specific ontology and the semantic metadata extracted by ASDI to answers questions in natural language format, and iii) a semantic search engine, which enhances traditional text-based searching by making use of the underlying ontologies and the extracted metadata. A semantic web portal application has been built, which illustrates the usage of this infrastructure

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