课题基金 / 基金详情

Linked Open Citation Database (LOC-DB) - Development of a Linked Open Data database for the indexing of citations of electronic and print media

Linked Open Citation Database (LOC-DB) - Development of a Linked Open Data database for the indexing of citations of electronic and print media
链接开放引文数据库 (LOC-DB) - 开发链接开放数据数据库,用于电子和印刷媒体引文索引
批准号:
311018540
负责人:
Professor Dr. Andreas Dengel
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research data and software (Scientific Library Services and Information Systems)
财政年份:
2016
资助国家:
德国
项目状态:
已结题
起止时间:
2015-12-31 至 2018-12-31

项目摘要

项目成果

Professor Dr. Andreas Dengel的其他基金

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中文摘要
翻译
LOC-DB项目将开发基于关联数据技术的即用即用工具和程序,使单个图书馆有可能为开放、分布式的引文编目基础设施作出有意义的贡献。该项目旨在证明,通过广泛自动化编目过程,通过定期捕获引文关系,有可能为学术搜索工具增加实质性的好处。这些数据将在语义网中可用,使未来的重复使用成为可能。此外,我们在有充分根据的成本效益分析中记录了数据的工作量、数量和质量。该项目将使用众所周知的信息提取方法,并对其进行调整,以适用于电子和印刷媒体中参考列表的任意布局。获得的原始数据将与现有的元数据源进行匹配和链接。此外,还将展示如何将这些数据集成到图书馆目录中。该系统将可以部署为在单个图书馆中高效使用,但原则上它也可以扩展为在网络中使用。
英文摘要
The LOC-DB project will develop ready-to-use tools and processes based on the linked-data-technology that make it possible for a single library to meaningfully contribute to an open, distributed infrastructure for cataloguing of citations. The project aims to prove that, by widely automating cataloguing processes, it is possible to add a substantial benefit to academic search tools by regularly capturing citation relations. These data will be made available in the semantic web to make future reuse possible. Moreover, we document effort, number and quality of the data in a well-founded cost-benefit analysis.The project will use well-known methods of information extraction and adapt them to work for arbitrary layouts of reference lists in electronic and print media. The obtained raw data will be aligned and linked with existing metadata sources. Moreover, it will be shown how these data can be integrated in library catalogues.The system will be deployable to use productively by a single library, but in principle it will also be scalable for using it in a network.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DeepBIBX: Deep Learning for Image Based Bibliographic Data Extraction
DeepBIBX:基于图像的书目数据提取的深度学习
DOI: 10.1007/978-3-319-70096-0_30
发表时间: 2017
期刊:
影响因子: --
作者: [Bhardwaj A, Mercier D, Dengel A, Ahmed S.]
通讯作者: Ahmed S.
Linked Open Citation Database: Enabling Libraries to Contribute to an Open and Interconnected Citation Graph
链接的开放引文数据库:使图书馆能够为开放且互连的引文图做出贡献
DOI: 10.1145/3197026.3197050
发表时间: 2018
期刊: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries
影响因子: --
作者: [Lauscher, Eckert, Scherp, Ansgar, Syed Tahseen Raza, Sheraz, Dengel, Andreas, Zumstein, Philipp, Annette]
通讯作者: Annette
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
用于推荐引文和主题标签的多模态对抗自动编码器
DOI: 10.1145/3209219.3209236
发表时间: 2018
期刊: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
影响因子: --
作者: [Florian, Vagliano, Iacopo, Scherp, Ansgar]
通讯作者: Ansgar
DOI: 10.1145/3127526.3127531
发表时间: 2017
期刊: Proceedings of the 6th International Workshop on Mining Scientific Publications
影响因子: --
作者: [Lauscher, Glavaš, Ponzetto, Simone Paolo, Eckert]
通讯作者: Eckert
metis II - Artificial intelligence methods for auto-completion of designs based on semantic building information (BIM) for supporting architects in early design phases.
"Scalable Methods of Text and Structure Recognition for the Full-Text Digitization of Historical Prints" Part 2: Layout Analysis
"Scalable Methods of Text and Structure Recognition for the Full-Text Digitization of Historical Prints" Part 1.B: Image Optimization
Sustaining Grass-roots Organizational Memories: Methods and Effects of Applying Managed Forgetting in Administrative Corporate Scenarios
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