International Journal on Digital Libraries最新文献

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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-05-05 DOI: 10.1007/s00799-023-00357-2

P. Roy

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

Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

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。

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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

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

引用次数: 1

A discovery system for narrative query graphs: entity-interaction-aware document retrieval. 叙述性查询图的发现系统：实体交互感知文档检索。

IF 1.5 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-04-24 DOI: 10.1007/s00799-023-00356-3

Hermann Kroll, Jan Pirklbauer, Jan-Christoph Kalo, Morris Kunz, Johannes Ruthmann, Wolf-Tilo Balke

Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one's information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user's intent. In contrast, distilling short narratives of the searchers' information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice. This paper extends our previous work by giving a comprehensive overview about the discovery system to realize narrative query graph retrieval.

在科学领域寻找相关出版物可能相当乏味：访问大型文档集通常意味着制定一个基于关键字的初始查询，然后进行许多改进，以检索一组足够完整但可管理的文档，以满足信息需求。由于基于关键字的搜索限制了研究人员将他们的信息需求表述为一组不相连的关键字，检索系统试图猜测每个用户的意图。相反，将搜索者信息需求的简短叙述提炼成简单而精确的实体交互图模式，可以提供精确搜索所需的所有信息。作为额外的好处，这样的图模式还可以以可变节点为特征，以灵活地允许对承担特定角色的实体进行不同的替换。对PubMed文档集的评估量化了我们新的实体交互感知搜索的精度增益。此外，我们还进行了专家访谈和问卷调查，以验证我们的系统在实践中的有用性。本文对实现叙述性查询图检索的发现系统进行了全面的概述，从而扩展了我们以前的工作。

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引用次数: 2

Approximate nearest neighbor for long document relationship labeling in digital libraries 数字图书馆长文档关系标注的近似最近邻算法

IF 1.5 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-04-15 DOI: 10.1007/s00799-023-00354-5

Peter Organisciak, Benjamin M. Schmidt, M. Durward

引用次数: 0

Referencing behaviours across disciplines: publication types and common metadata for defining bibliographic references 跨学科引用行为:用于定义书目引用的出版物类型和通用元数据

IF 1.5 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-03-27 DOI: 10.1007/s00799-023-00351-8

Erika Alves dos Santos, S. Peroni, M. L. Mucheroni

引用次数: 1

Scientific document processing: challenges for modern learning methods. 科学文档处理：现代学习方法面临的挑战。

IF 1.5 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-03-24 DOI: 10.1007/s00799-023-00352-7

Abhinav Ramesh Kashyap, Yajing Yang, Min-Yen Kan

Neural network models enjoy success on language tasks related to Web documents, including news and Wikipedia articles. However, the characteristics of scientific publications pose specific challenges that have yet to be satisfactorily addressed: the discourse structure of scientific documents crucial in scholarly document processing (SDP) tasks, the interconnected nature of scientific documents, and their multimodal nature. We survey modern neural network learning methods that tackle these challenges: those that can model discourse structure and their interconnectivity and use their multimodal nature. We also highlight efforts to collect large-scale datasets and tools developed to enable effective deep learning deployment for SDP. We conclude with a discussion on upcoming trends and recommend future directions for pursuing neural natural language processing approaches for SDP.

神经网络模型在与网络文档相关的语言任务上取得了成功，包括新闻和维基百科文章。然而，科学出版物的特点带来了尚未令人满意地解决的具体挑战：在学术文献处理（SDP）任务中至关重要的科学文献的话语结构、科学文献的相互联系性质及其多模式性质。我们调查了应对这些挑战的现代神经网络学习方法：那些能够建模话语结构及其相互关联性并利用其多模态性质的方法。我们还强调了收集大规模数据集的努力，以及为实现SDP的有效深度学习部署而开发的工具。最后，我们讨论了即将到来的趋势，并为SDP的神经自然语言处理方法提出了未来的发展方向。

引用次数: 2

The digitization of historical astrophysical literature with highly localized figures and figure captions 具有高度本地化图形和图形说明的历史天体物理文献的数字化

Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-03-22 DOI: 10.1007/s00799-023-00350-9

Jill P. Naiman, Peter K. G. Williams, Alyssa Goodman

Scientific articles published prior to the “age of digitization” in the late 1990s contain figures which are “trapped” within their scanned pages. While progress to extract figures and their captions has been made, there is currently no robust method for this process. We present a YOLO-based method for use on scanned pages, after they have been processed with Optical character recognition (OCR), which uses both grayscale and OCR features. We focus our efforts on translating the intersection-over-union (IOU) metric from the field of object detection to document layout analysis and quantify “high localization” levels as an IOU of 0.9. When applied to the astrophysics literature holdings of the NASA astrophysics data system, we find F1 scores of 90.9% (92.2%) for figures (figure captions) with the IOU cut-off of 0.9 which is a significant improvement over other state-of-the-art methods.

在20世纪90年代末“数字化时代”之前发表的科学文章中，有一些数字被“困”在扫描页面中。虽然在提取数字及其说明文字方面取得了进展，但目前尚无可靠的方法来处理这一过程。我们提出了一种基于yolo的方法，用于扫描页面，在使用光学字符识别(OCR)处理后，该方法同时使用灰度和OCR特征。我们专注于将目标检测领域的交叉-超联合(IOU)度量转换为文件布局分析，并将“高本地化”水平量化为IOU为0.9。当应用于NASA天体物理数据系统的天体物理文献时，我们发现数字(图注)的F1得分为90.9% (92.2%)，IOU截止值为0.9，这比其他最先进的方法有了显着提高。

引用次数: 1

Beyond translation: engaging with foreign languages in a digital library 超越翻译:在数字图书馆中学习外语

IF 1.5 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

International Journal on Digital Libraries

Pub Date : 2023-03-19 DOI: 10.1007/s00799-023-00349-2

G. Crane, Alison Babeu, Lisa M. Cerrato, Amelia Parrish, Carolina Penagos, Farnoosh Shamsian, James Tauber, Jake Wegner

引用次数: 4

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