Integrating Similarity Retrieval and Skyline Exploration Via Relevance Feedback

Abstract

Abstract. Similarity retrieval have been widely used in many practical search applications. A similarity query model can be viewed as a logical combination of a set of similarity predicates. A user can initialize a query model, but model parameters or the model itself may be inadequately specified. As a result, a retrieval system cannot guarantee that it has presented all the relevant tuples to the user. In this paper, we propose a framework that integrates the similarity retrieval and skyline exploration. Using the relevance feedback as a way to constrain the search space, our framework can intelligently explore only a necessary portion of data that contains all the relevant tuples. Our framework is also flexible enough to incorporate model refinement techniques to retrieving relevant results as early as possible.

FlexIQ: A flexible interactive Querying Framework by Exploiting the Skyline Operator

“…This work is inspired by the previous works found in (Huang et al, 2008), (Herschel and Hernández, 2010), (Tran and Chan, 2010), (He and Lo, 2012), (Ma et al, 2006), (Liu et al, 2010), (Koudas et al, 2006) and (Ma and Mehrotra, 2007). In (Huang et al, 2008), Huang et al and in (Herschel and Hernández, 2010), Herschel et al propose to modify the original tuple values in the database so that missing tuples become part of the query output.…”

“…what is missing). In (Ma and Mehrotra, 2007), Ma et al propose a framework that combines the positive aspects of both similarity retrieval and skyline retrieval into one single technique so that the user can retrieve results in the order of relevance. In (Liu et al, 2010), Liu et al collect false positives (which we call unexpected information in this paper) which are identified by users to modify the initial rules in information extraction setting.…”

User Feedback Based Query Refinement by Exploiting Skyline Operator

“…Now, let r be the boundary between Q(D) and Q'(D), (and~ be the minimal feedback for the unexpected and expected tuples respectively. Then, we define our boundary adjustment problem as follows: 11 The basic idea of our boundary adjustment algorithm is checking the pairwise dominance between feedback tuples and boundary tuples of the original query Q.…”

“…In [11], Ma et al propose a framework that combines the positive aspects of both similarity retrieval and skyline retrieval into one single technique so that the user can retrieve results in the order of relevance. None of the above models treats both unexpected and expected feedback.…”