Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.1114Nsciescopu

iKernel: Exact Indexing for Support Vector Machines

SVM (Support Vector Machine) is a well-established machine learning methodology popularly used for learning classification, regression, and ranking functions. Especially, SVM for rank learning has been applied to various applications including search engines or relevance feedback systems. A ranking function F learned by SVM becomes the query in some search engines: A relevance function F is learned from the user's feedback which expresses the user's search intention, and top-k results are found by evaluating the entire database by F. This paper proposes an exact indexing solution for the SVM function queries, which is to find top-k results without evaluating the entire database. Indexing for SVM faces new challenges, that is, an index must be built on the kernel space (SVM feature space) where (1) data points are invisible and (2) the distance function changes with queries. Because of that, existing top-k query processing algorithms, or existing metric-based or reference-based indexing methods are not applicable. We first propose key geometric properties of the kernel space - ranking instability and ordering stability - which is crucial for building indices in the kernel space. Based on them, we develop an index structure iKernel and processing algorithms. We then present clustering techniques in the kernel space to enhance the pruning effectiveness of the index. According to our experiments, iKernel is highly effective overall producing 1-5% of evaluation ratio on large data sets. (C) 2013 Elsevier Inc. All rights reserved.X1122sciescopu

Exact Indexing for Support Vector Machines

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