Lessons Learned in Building Linked Data for the American Art Collaborative
Lessons Learned in Building Linked Data for the American Art Collaborative
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为美国艺术合作组织构建关联数据的经验教训
DOI：
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发表时间：
2017
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通讯作者：
Yixiang Yao
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作者：
Craig A. Knoblock;Pedro A. Szekely;E. Fink;D. Degler;D. Newbury;Robert Sanderson;Kate Blanch;Sara Snyder;Nilay Chheda;Nimesh Jain;Ravi Raju Krishna;Nikhila Begur Sreekanth;Yixiang Yao
关联数据已成为发布和共享文化遗产数据的首选方法。博物馆面临的主要挑战之一是，事实上的标准本体（CIDOC CRM）是复杂的，博物馆缺乏语义Web技术的专业知识。在本文中，我们描述的方法和工具，我们用来创建5-星星关联数据的14个美国艺术博物馆与团队的12名计算机科学专业的学生和30名代表的博物馆谁大多缺乏专业知识的语义Web技术。该项目历时18个月完成，生成了99个映射文件和9，357个艺术家链接，总共生成了2，714个R2 RML规则和970万个三元组。更重要的是，该项目产生了一些用于生成高质量关联数据的开放源码工具，并产生了一套可用于未来项目的经验教训。
Linked Data has emerged as the preferred method for publishing and sharing cultural heritage data. One of the main challenges for museums is that the defacto standard ontology (CIDOC CRM) is complex and museums lack expertise in semantic web technologies. In this paper we describe the methodology and tools we used to create 5-star Linked Data for 14 American art museums with a team of 12 computer science students and 30 representatives from the museums who mostly lacked expertise in Semantic Web technologies. The project was completed over a period of 18 months and generated 99 mapping files and 9,357 artist links, producing a total of 2,714 R2RML rules and 9.7M triples. More importantly, the project produced a number of open source tools for generating high-quality linked data and resulted in a set of lessons learned that can be applied in future projects.