Year: 2011
Type: preprint
Abstract: Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.
Source: arXiv (Cornell University)
Authors: Hollink, Vera, de Vries, Arjen
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1781284824