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: --
• 依托单位: Universitätsbibliothek Mannheim
• 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
• 起止时间: 2015-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/311018540?language=en
• 关键词: Linked; Open; Citation; Database; LOC

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.

LOC-DB项目将开发基于链接数据技术的现成工具和流程，使单个图书馆能够为开放、分布式的引文编目基础设施做出有意义的贡献。该项目旨在证明，通过广泛自动化编目过程，有可能通过定期捕获引文关系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提供，以使将来的重用成为可能。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，并使其适应电子和印刷媒体中参考书目的任意布局。获得的原始数据将与现有的元数据源进行对齐和链接。此外，还将说明如何将这些数据纳入图书馆目录。该系统可以部署到单个库中进行高效使用，但原则上它也可以扩展到网络中使用。

项目成果

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
• 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
• 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
• 作者: Florian;Vagliano;Iacopo;Scherp;Ansgar
• 通讯作者: Ansgar

Investigating Convolutional Networks and Domain-Specific Embeddings for Semantic Classification of Citations

研究用于引文语义分类的卷积网络和特定领域嵌入

• DOI: 10.1145/3127526.3127531
• 发表时间: 2017
• 期刊: Proceedings of the 6th International Workshop on Mining Scientific Publications
• 作者: Lauscher;Glavaš;Ponzetto;Simone Paolo;Eckert
• 通讯作者: Eckert

Von der Schneeflocke zur Lawine: Möglichkeiten der Nutzung freier Zitationsdaten in Bibliotheken

从雪花到雪崩：在图书馆中使用免费引文数据的可能性

• DOI: 10.5282/o-bib/2017h4s127-136
• 发表时间: 2017
• 作者: Annette