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A model-based method to improve the quality of ranking in keyword search systems using pseudo-relevance feedback

Keyword search has been known as an attractive search over databases. One of the challenges in keyword search is to rank the answers (subgraphs) of a keyword query in order to their relevance to the query. Most of the previous studies in this area are highly heuristic ranking functions which were proposed based on a confined analysis of the characteristics of answers. These functions usually reveal serious effectiveness problems due to their failure in recognising the conceptual differences between relatively similar answers. In this article, we propose a novel model-based method to improve the ranking accuracy of answers to a keyword query using pseudo-relevance feedback. The proposed method is built upon a carefully designed model for an answer called Structure-Aware Relevance Model which is estimated based on the textual and structural characteristics of the answer. In this article, we also study how to effectively select from feedback answers those words that are focused on the query topic based on placement and importance of words in the nodes of feedback answers. Extensive experiments conducted on a standard evaluation framework with three real-world datasets confirm the effectiveness of the proposed method.