Modeling concepts and their relationships for corpus-based query auto-completion

Open Computer Science

Caputo Annalina¹  Semeraro Giovanni²  Basile Pierpaolo²  Rossiello Gaetano³ 

[1] ADAPT centre, School of Computer Science and Statistics, Trinity College Dublin, Ireland.;Department of Computer Science, University of Bari Aldo Moro, Bari, Italy.;IBM Research AI, Thomas J. Watson Research Center, Yorktown Heights, NY, USA.;

关键词: query auto-completion;    information retrieval;    information extraction;    probabilistic graphical model;   

DOI  :  10.1515/comp-2019-0015

来源: DOAJ

【 摘 要 】

Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.

【 授权许可】

Unknown