|Special Issue on Structured, Social and Crowd-sourced Data on the Web|
|Written by VLDB Journal Team|
|Sunday, 11 April 2010 06:11|
Aims and Scope
The abundance of structured and social data on the Web coupled with ability to solicit feedback from crowds has the potential of changing the way we search for information and enabling new classes of applications on the Web. This special issue of the VLDB Journal will feature original contributions that advance the state of the art in this topic area. Broadly, the special issue is concerned with methods for analyzing and serving structured data on the Web and methods for enhancing data by soliciting feedback from crowds.
Structured data appears on the Web in several forms, including hidden Web sources exposed through HTML form interfaces, tables, lists and pages with repeating semi-structured cards. Current research efforts for leveraging this data include approaches for extracting and combining results from multiple sources, for surfacing the deep web, and for exposing data through RDF repositories with rich linking, possibly exploiting existing knowledge bases for data annotation and integration.
Crowd-sourcing and social data are increasingly popular methods for improving search results and enhancing the quality of data on the Web. Social data has huge potential to re-rank and enrich pages and content based on what the user's friends have visited or recommended previously. Crowd-sourcing can be used to answer questions that are inherently hard for machines but can be handled relatively easily with human input. The main challenges in these areas concern the quality assessment of the additional signals and blending socially-promoted results with results generated by traditional algorithms.
Topics of Interest
Topics of interest for this special issue include (but are not limited to):
Marco Brambilla, Politecnico di Milano, Italy ()
|Last Updated on Thursday, 13 September 2012 13:43|