There is great interest in supporting imprecise queries over databases today. To support such queries, the system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available to it (the database schema, value distributions, natural language models etc). Often, systems will also have a user-interaction log available, which can supplement their model based on their own intrinsic resources. This leads to a problem of how best to combine the system's prior ranking with insight derived from the user-interaction log. Statistical inference techniques such as maximum likelihood or Bayesian updates from a subjective prior turn out not to apply in a straightforward way due to possible noise from user search behavior and to encoding biases endemic to the system's models. In this paper, we address such learning problems in interactive data retrieval, with specific focus on type classification for user-specified query terms. We develop a novel Bayesian smoothing algorithm, Bsmooth, which is simple, fast, flexible and accurate. We analytically establish some desirable properties and show, through experiments against an independent benchmark, that the addition of such a learning layer performs much better than standard methods.
. Some ad-hoc practices in the keyword search literature that have been criticized @cite_4 @cite_14 @cite_21 , and that we have strived not to incur in are: Existing scoring functions have become increasingly complex while the added value obscure @cite_21 . We have studied and shown in detail the added value of implicit feedback as a DB-extrinsic scoring to be combined by Bsmooth with any existing DB-intrinsic scoring.
@cite_14 @cite_4 @cite_21
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Keyword search (KWS) over relational databases has recently received significant attention. Many solutions and many prototypes have been developed. This task requires addressing many issues, including robustness, accuracy, reliability, and privacy. An emerging issue, however, appears to be performance related: current KWS systems have unpredictable running times. In particular, for certain queries it takes too long to produce answers, and for others the system may even fail to return (e.g., after exhausting memory). In this paper we argue that as today's users have been "spoiled" by the performance of Internet search engines, KWS systems should return whatever answers they can produce quickly and then provide users with options for exploring any portion of the answer space not covered by these answers. Our basic idea is to produce answers that can be generated quickly as in today's KWS systems, then to show users query forms that characterize the unexplored portion of the answer space. Combining KWS systems with forms allows us to bypass the performance problems inherent to KWS without compromising query coverage. We provide a proof of concept for this proposed approach, and discuss the challenges encountered in building this hybrid system. Finally, we present experiments over real-world datasets to demonstrate the feasibility of the proposed solution. The prevalence of free text search in web search engines has inspired recent interest in keyword search on relational databases. Whereas relational queries formally specify matching tuples, keyword queries are imprecise expressions of the user’s information need. The correctness of search results depends on the user’s subjective assessment. As a result, the empirical evaluation of a keyword retrieval system’s effectiveness is essential. In this paper, we examine the evolving practices and resources for effectiveness evaluation of keyword searches on relational databases. We compare practices with the longer-standing full-text evaluation methodologies in information retrieval. In the light of this comparison, we make some suggestions for the future development of the art in evaluating keyword search effectiveness. Extending the keyword search paradigm to relational data has been an active area of research within the database and IR community during the past decade. Many approaches have been proposed, but despite numerous publications, there remains a severe lack of standardization for the evaluation of proposed search techniques. Lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what advantages are proffered by different approaches. In this paper, we present the most extensive empirical performance evaluation of relational keyword search techniques to appear to date in the literature. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small data sets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous evaluations; our analysis indicates that most of these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the unacceptable performance of these search techniques and underscores the need for standardization in evaluations--standardization exemplified by the IR community.