A Robust Retrieval Engine for Proximal and Structural Search

This paper describes a new ranked retrieval model that enables proximal and structural search for structured texts. We extend the model proposed in Region Algebra to be robust by i) incorporating the idea of rankedness in keyword-based search, and ii) expanding queries. While in ordinary ranked retrieval models relevance measures are computed in terms of words, our model assumes that they are defined in more general structural fragments, i.e., extents (continuous fragments in a text) proposed in Region Algebra. We decompose queries into subqueries to allow the system not only to retrieve exactly matched extents but also to retrieve partially matched ones. Our model is robust like keyword-based search, and also enables us to specify the structural and linear positions in texts as done by Region Algebr