text: 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.
  @cite_11: Citations management is an important task in managing digital libraries. Citations provide valuable information e.g., used in evaluating an author's influences or scholarly quality (the impact factor of research journals). But although a reliable and effective autonomous citation management is essential, manual citation management can be extremely costly. Automatic citation mining on the other hand is a non-trivial task mainly due to non-conforming citation styles, spelling errors and the difficulty of reliably extracting text from PDF documents. In this paper we propose a novel rule-based autonomous citation mining technique, to address this important task. We define a set of common heuristics that together allow to improve the state of the art in automatic citation mining. Moreover, by first disambiguating citations based on venues, our technique significantly enhances the correct discovery of citations. Our experiments show that the proposed approach is indeed able to overcome limitations of current leading citation indexes such as ISI Web of Knowl- edge, Citeseer and Google Scholar. Categories and Subject Descriptors H.3.7 (Digital Libraries); System issues: H.3.3 (Information Search and Retrieval); Retrieval models: I.1.2 (Algorithms); Analysis of algorithms: I.2.8 (Problem Solving, Control Methods, and Search);
summary: The problem that we aim to solve involves the two subtasks of 1. identifying dataset references in an article, and then 2. finding at least one correct match for each of these identified references. Literature citation mining is the process of determining the number of citations that a specific article receives. It constructs a literature citation network, which can be used for detecting the impact of an article @cite_11 . Citation mining can usually be handled by three subtasks. First, literature references should be extracted from the bibliography section of a document, and afterwards, metadata extraction should be applied on the references extracted in the first phase. Finally, each reference should be linked to the cited article by using the metadata extracted in the second step @cite_11 .