Query‐performance prediction for effective query routing in domain‐specific repositories

Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.

Supporting Complex Information-Seeking Tasks with Implicit Constraints

“…In other words, query performance predictors predict the quality of retrieved items w.r.t to the query. QPP methods have been used in different applications such as query reformulation, query routing, and in intelligent systems [74,79]. QPP methods are a promising indicator of retrieval performance and are categorized into pre-retrieval, and post-retrieval methods [16].…”

“…Another important aspect we touch on is a simple, straightforward evaluation of the proposed approach. We adopt pre-retrieval metrics [e.g., 74,79] as a means to evaluate the extent to which refinement to the complex request afforded by the IA better represents the actual user intent, or narrows down the search space. Our evaluation demonstrates that a better formulated complex request results in a more reliable and accurate retrieval process.…”