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)

Abstract

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
Pages207-222
Number of pages16
Volume7538
DOIs
Publication statusPublished - 2012
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7538
ISSN (Print)03029743
ISSN (Electronic)16113349

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  • Cite this

    Bozzon, A., Brambilla, M., Ceri, S., & Mauri, A. (2012). Extending search to crowds: A model-driven approach. In Search Computing: Broadening Web Search (Vol. 7538, pp. 207-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7538). https://doi.org/10.1007/978-3-642-34213-4_14