User and Context-Aware Quality Filters Based on Web Metadata Retrieval
Ricardo Barros (Federal University of Rio de Janeiro, Brazil), Geraldo Xexéo (Federal University of Rio de Janeiro, Brazil), Wallace A. Pinheiro (Federal University of Rio de Janeiro, Brazil) and Jano de Souza (Federal University of Rio de Janeiro, Brazil)
Copyright: © 2008
Due to the amount of information on the Web being so large and being of varying levels of quality, it is becoming increasingly difficult to find precisely what is required on the Web, particularly if the information consumer does not have precise knowledge of his or her information needs. On the Web, while searching for information, users can find data that is old, imprecise, invalid, intentionally wrong, or biased, due to this large amount of available data and comparative ease of access. In this environment users constantly receive useless, outdated, or false data, which they have no means to assess. This chapter addresses the issues regarding the large amount and low quality of Web information by proposing a methodology that adopts user and context-aware quality filters based on Web metadata retrieval. This starts with an initial evaluation and adjusts it to consider context characteristics and user perspectives to obtain aggregated evaluation values.