Extending search to crowds: A model-driven approach

Alessandro Bozzon, Marco Brambilla, Stefano Ceri, Andrea Mauri

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

2 Citations (Scopus)


In many settings, the human opinion provided by an expert or knowledgeable user can be more useful than factual information retrieved by a search engine. Search systems do not capture the subjective opinions and recommendations of friends, or fresh, online-provided information that require contextual or domain-specific expertise. Search results obtained from conventional search engines can be complemented by crowdsearch, an online interaction with crowds, selected among friends, experts, or people who are presently at a given location; an interplay between conventional and search-based queries can occur, so that the two search methods can support each other. In this paper, we use a model-driven approach for specifying and implementing a crowdsearch application; in particular we define two models: the "Query Task Model", representing the meta-model of the query that is submitted to the crowd and the associated answers; and the "User Interaction Model", showing how the user can interact with the query model to fulfil her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation, thus leading to quick prototyping of crowd-search applications.

Original languageEnglish
Title of host publicationSearch Computing: Broadening Web Search
Number of pages16
Publication statusPublished - 2012
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Dive into the research topics of 'Extending search to crowds: A model-driven approach'. Together they form a unique fingerprint.

Cite this