将期刊的女性编辑与维基数据知识图谱连接起来

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Semantic Web Pub Date : 2022-09-29 DOI:10.3233/sw-222845
Katherine Thornton, Kenneth Seals-Nutt, Marianne Van Remoortel, Julie M. Birkholz, P. D. Potter
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引用次数: 0

摘要

故事是叙述和分享过去的重要工具。要讲一个故事,必须把关于人物、地点、时期和事物的各种信息放在一起。我们在这里详细介绍一台机器如何通过语义网的力量,将分散的、不同的材料和信息汇编成故事。通过WeChangEd关于1710-1920年欧洲期刊女性编辑的研究项目,我们详细介绍了如何从档案到结构化数据模型和关系数据库,再到维基数据,再到使用故事服务API来生成与人物、组织和期刊相关的多媒体故事。随着越来越多的人文主义者、社会科学家和其他研究人员选择将他们的数据贡献给维基数据,我们都将受益。随着研究人员增加数据,我们可以提出的关于我们所提供的数据的问题的广度和复杂性将增加。构建应用程序,将来自维基数据的数据联合起来,使我们能够利用一个通用的知识图谱,其中包含越来越多的学术文献参考。使用维基数据社区开发的框架,我们可以快速提供交互式站点,帮助我们吸引新的受众。我们在这里详细介绍的这一过程可能会引起其他研究人员和文化遗产机构的兴趣,这些研究人员和文化遗产机构正在寻求基于网络的展示方式,以从他们的数据中讲述故事。
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Linking women editors of periodicals to the Wikidata knowledge graph
Stories are important tools for recounting and sharing the past. To tell a story one has to put together diverse information about people, places, time periods, and things. We detail here how a machine, through the power of Semantic Web, can compile scattered and diverse materials and information to construct stories. Through the example of the WeChangEd research project on women editors of periodicals in Europe from 1710–1920 we detail how to move from archive, to a structured data model and relational database, to Wikidata, to the use of the Stories Services API to generate multimedia stories related to people, organizations and periodicals. As more humanists, social scientists and other researchers choose to contribute their data to Wikidata we will all benefit. As researchers add data, the breadth and complexity of the questions we can ask about the data we have contributed will increase. Building applications that syndicate data from Wikidata allows us to leverage a general purpose knowledge graph with a growing number of references back to scholarly literature. Using frameworks developed by the Wikidata community allows us to rapidly provision interactive sites that will help us engage new audiences. This process that we detail here may be of interest to other researchers and cultural heritage institutions seeking web-based presentation options for telling stories from their data.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
期刊最新文献
Using Wikidata lexemes and items to generate text from abstract representations Editorial: Special issue on Interactive Semantic Web Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1 Special Issue on Semantic Web for Industrial Engineering: Research and Applications Declarative generation of RDF-star graphs from heterogeneous data
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