Keyword search on structured and semi-structured data

TLDR

Empowering users to access databases using simple keywords can relieve them from the steep learning curve of mastering a structured query language and understanding complex, rapidly evolving data schemas. The tutorial reviews state‑of‑the‑art techniques for keyword search on structured and semi‑structured data and outlines future research challenges and opportunities. It covers data models such as relational, XML, graph, streams, and workflows, and discusses applications built on keyword search including database selection, query generation, and analytical processing. The authors identify challenges and opportunities for future research to advance keyword search on structured and semi‑structured data.

Abstract

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.