Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
Many different machine learning approaches have been employed for extracting metadata, and in a few cases also for detecting dataset references. For example, Zhang2006 @cite_17 and Han2003 @cite_0 proposed keyword extraction methods based on support vector machines (SVM).
@cite_0 @cite_17
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Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a support vector machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. The method first classifies each line of the header into one or more of 15 classes. An iterative convergence procedure is then used to improve the line classification by using the predicted class labels of its neighbor lines in the previous round. Further metadata extraction is done by seeking the best chunk boundaries of each line. We found that discovery and use of the structural patterns of the data and domain based word clustering can improve the metadata extraction performance. An appropriate feature normalization also greatly improves the classification performance. Our metadata extraction method was originally designed to improve the metadata extraction quality of the digital libraries Citeseer [S. , (1999)] and EbizSearch [Y. , (2003)]. We believe it can be generalized to other digital libraries. Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) have been used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents MRSMO, a MapReduce based distributed SVM algorithm for automatic image annotation. The performance of the MRSMO algorithm is evaluated in an experimental environment. By partitioning the training dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computers, the MRSMO algorithm reduces the training time significantly while maintaining a high level of accuracy in both binary and multiclass classifications.