Improving the effectiveness of keyword search in databases using query logs. (May 2019)

• Record Type: Journal Article
• Authors: Yu, Ziqiang; Abraham, Ajith; Yu, Xiaohui; Liu, Yang; Zhou, Jing; Ma, Kun
• Is Part Of: Engineering applications of artificial intelligence. Volume 81(2019)
• Page Start: 169
• Page End: 179
• Publication Date: 2019-05
• Subjects: Keyword search -- Top-k -- Query log -- Relational database; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.01.017
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Using query logs to enhance user experience has been extensively studied in the Web IR literature. However, in the area of keyword search on structured data (relational databases in particular), most existing works have focused on improving search result quality via designing better scoring functions, without giving explicit consideration to query logs. However, query logs can reflect the user preferences, so our work taps into the wealth of information contained in query logs and aims to enhance the search effectiveness by explicitly taking into account the log information when ranking the query results. Different from existing approaches only relying on a schema graph or a data graph, our work designs a comprehensive solution based on both the schema graph and the data graph for discovering top- k results with two stages. First, we identify top- k candidate networks with a query-log-aware ranking strategy by employing the largest frequent subtrees mined from query logs. Since a candidate network usually corresponds to multiple joined tuple trees, we further rank these joined tuple trees with the PageRank principle based on the data graph in the second stage. Finally, user studies on a real dataset validate the effectiveness of the proposed ranking strategy. Graphical abstract: