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):

• Modeling, deploying, exposing and using search functionalities as services
• Query languages and efficient execution models for ranking, ordering, and chunking results extracted from data sources
• Languages, platforms, methods, and best practices for composing search services over structured web data sources
• Query languages and methods for integrating deep web data sources
• Mashup platforms and practices for deep web data
• Wrapping technologies, languages, and methods for data-intensive web sites
• Methods for preparing and labeling data to support search applications
• Explaining and enriching search results using semantics
• Algorithms and tools for search and exploration over linked and semantically-enriched data
• Employing the "wisdom of crowds" for ranking search results
• Integrated systems for crowd-sourcing and crowd-searching over conventional and social platforms
• Leveraging semantics in search and measuring its impact
• Measuring the relevance of deep web information
• Measuring the effectiveness of crowd-enabled search tasks
• Applications of search on structured and social information

Guest Editors

Marco Brambilla, Politecnico di Milano, Italy ()
Stefano Ceri, Politecnico di Milano, Italy ()
Alon Halevy, Google, California, USA, ()

Important Dates

• Paper submissions: September 15th, 2012
• First round notification: January 15th, 2013
• Revised versions: April 1st, 2013
• Second round notifications: May 1st, 2013
• Final version: June 1st, 2013
• Publication in August or October, 2013