In this paper, the goal is harvesting all documents matching a given (entity) query from a deep web source. The objective is to retrieve all information about for instance "Denzel Washington", "Iran Nuclear Deal", or "FC Barcelona" from data hidden behind web forms. Policies of web search engines usually do not allow accessing all of the matching query search results for a given query. They limit the number of returned documents and the number of user requests. In this work, we propose a new approach which automatically collects information related to a given query from a search engine, given the search engine's limitations. The approach minimizes the number of queries that need to be sent by applying information from a large external corpus. The new approach outperforms existing approaches when tested on Google, measuring the total number of unique documents found per query.
|Title of host publication||Proceedings of the 1st International Workshop on Knowledge Discovery on the Web, KDWEB 2015|
|Editors||Giuliano Armano, Alessandro Bozzon, Alessandro Giuliani|
|Place of Publication||Aachen|
|Number of pages||7|
|Publication status||Published - Sep 2015|
|Name||CEUR Workshop Proceedings|