Keyword Query Reformulation on Structured Data

Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of keyword query reformulation in the structured data scenario. These reformulated queries provide alternative descriptions of original input. They could better capture users' information need and guide users to explore related items in the target structured data. We propose an automatic keyword query reformulation approach by exploiting structural semantics in the underlying structured data sources. The reformulation solution is decomposed into two stages, i.e., offline term relation extraction and online query generation. We first utilize a heterogenous graph to model the words and items in structured data, and design an enhanced Random Walk approach to extract relevant terms from the graph context. In the online query reformulation stage, we introduce an efficient probabilistic generation module to suggest substitutable reformulated queries. Extensive experiments are conducted on a real-life data set, and our approach yields promising results.