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International Journal on Digital Libraries最新文献

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A detailed library perspective on nearly unsupervised information extraction workflows in digital libraries 数字图书馆中几乎无监督的信息提取工作流程的详细图书馆视角
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-06-13 DOI: 10.1007/s00799-023-00368-z
H. Kroll, Jan Pirklbauer, Florian Plötzky, Wolf-Tilo Balke
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引用次数: 0
Special Issue: Epigraphy and Paleography: Bringing Records from the Distant Past to the Present 特刊:铭文和古文字:从遥远的过去到现在带来的记录
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-06-01 DOI: 10.1007/s00799-023-00371-4
Stephen M. Griffin
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引用次数: 0
From stone to silicon: technical advances in epigraphy 从石头到硅:金石学的技术进步
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-06-01 DOI: 10.1007/s00799-023-00362-5
W. Seales, Christy Chapman
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引用次数: 0
Coverage and similarity of bibliographic databases to find most relevant literature for systematic reviews in education 覆盖范围和相似的书目数据库，以找到最相关的文献在教育系统综述
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-05-24 DOI: 10.1007/s00799-023-00364-3
Tamara Heck, C. Keller, Marc Rittberger
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引用次数: 0
Correction: Beyond translation: engaging with foreign languages in a digital library 更正:超越翻译:在数字图书馆中接触外语
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-05-19 DOI: 10.1007/s00799-023-00365-2
G. Crane, Alison Babeu, Lisa M. Cerrato, Amelia Parrish, Carolina Penagos, Farnoosh Shamsian, James Tauber, Jake Wegner
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引用次数: 0
Self-training involving semantic-space finetuning for semi-supervised multi-label document classification 基于语义空间调优的半监督多标签文档分类自训练
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-05-11 DOI: 10.1007/s00799-023-00355-4
Zhewei Xu, M. Iwaihara
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引用次数: 0
DETEXA: declarative extensible text exploration and analysis through SQL. DETEXA：通过SQL进行声明性可扩展文本探索和分析。
IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
International Journal on Digital Libraries
Pub Date : 2023-05-10 DOI: 10.1007/s00799-023-00358-1
Yannis Foufoulas, Eleni Zacharia, Harry Dimitropoulos, Natalia Manola, Yannis Ioannidis

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the exponential increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources. In this paper, we introduce a text analysis framework implemented in extended SQL that exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which include data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup, up to three times faster, in common use cases compared to other popular approaches.

通过文本挖掘技术丰富元数据正成为数字图书馆中最重要的任务之一。由于开放获取出版物呈指数级增长，出现了一些新的挑战。原始数据通常是大的、非结构化的，并且来自异构数据源。在本文中，我们介绍了一个在扩展SQL中实现的文本分析框架，该框架利用了现代数据库管理系统的可扩展性特征。该框架的目的是提供构建高性能端到端文本挖掘管道的机会，该管道包括数据采集、清理、处理和文本分析。SQL之所以被选中，是因为它的声明性提供了快速实验和构建API的能力，使领域专家可以通过易于使用的图形界面编辑文本挖掘工作流。我们的实验分析表明，与其他流行方法相比，在常见用例中，所提出的框架非常有效，并实现了显著的加速，速度高达三倍。
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引用次数: 0
Predicting answer acceptability for question-answering system 预测问答系统的答案可接受性
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-05-05 DOI: 10.1007/s00799-023-00357-2
P. Roy
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引用次数: 0
Deep author name disambiguation using DBLP data 使用DBLP数据的深度作者姓名消歧
Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-05-04 DOI: 10.1007/s00799-023-00361-6
Zeyd Boukhers, Nagaraj Bahubali Asundi
Abstract In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
在学术界，科学家的数量每年都在增加，同名作者的数量也在增加。因此，将新发表的论文分配给各自的作者是一项挑战。因此，作者姓名歧义被认为是数字图书馆中一个重要的开放性问题。本文提出了一种作者姓名消歧方法，通过利用作者的共同作者和研究领域，将作者姓名与其现实世界的实体联系起来。为此，我们使用了从DBLP存储库收集的数据，该存储库包含约260万共同作者撰写的500多万条书目记录。我们的方法首先对姓氏和名字首字母相同的作者进行分组。每个组中的作者通过捕捉与其共同作者和研究领域的关系来识别，由通讯作者的有效出版物的标题表示。为此，我们训练了一个神经网络模型，该模型从共同作者和标题的表示中学习。我们通过在大型数据集上进行广泛的实验来验证我们方法的有效性。
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引用次数: 0
Towards automated meta-review generation via an NLP/ML pipeline in different stages of the scholarly peer review process 在学术同行评审过程的不同阶段，通过NLP/ML管道实现自动元评审生成
IF 1.5 Q1 Social Sciences
International Journal on Digital Libraries
Pub Date : 2023-04-24 DOI: 10.1007/s00799-023-00359-0
Asheesh Kumar, Tirthankar Ghosal, Saprativa Bhattacharjee, Asif Ekbal
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引用次数: 1
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