TY - CHAP
T1 - Extending search to crowds
T2 - A model-driven approach
AU - Bozzon, Alessandro
AU - Brambilla, Marco
AU - Ceri, Stefano
AU - Mauri, Andrea
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893116735&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34213-4_14
DO - 10.1007/978-3-642-34213-4_14
M3 - Chapter
AN - SCOPUS:84893116735
SN - 9783642342127
VL - 7538
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 207
EP - 222
BT - Search Computing: Broadening Web Search
ER -