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Social Sciences, Social Data and the Semantic Web (S3W)

社会科学、社会数据和语义网 (S3W)

基本信息

批准号：
ES/R009058/1
负责人：
Susan Halford
金额：
$ 25.7万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FR009058%2F1
关键词：
Social Sciences Data Semantic Web

项目摘要

Recent years have seen phenomenal growth in quantity and range of digital data that might be used for social research. The ESRC has already invested in harnessing administrative and business data as well as 'new and emerging forms of data' (e.g. social media and sensor data) for the social sciences. Now a new opportunity arises. 'Semantic linked data' (SLD) offers a new method for structuring and organizing digital data, which promises to have a profound effect on research capacity for data linkage and analysis across multiple, heterogeneous sources, at hitherto unimaginable speed and scale. Indeed, within the Computer Sciences, the proponents of SLD argue that if data are published following shared standards and protocols the Web will be transformed from a library of documents into a single linked data base, described as the 'semantic web'. The value of data linkage is already well established in the social sciences, but existing methods are labour intensive, involve the retrospective matching of records, across small numbers of data sets, and are done to address particular pre-determined questions with the linkage made for that specific purpose. In contrast, SLD techniques focus on the prospective production of data to allow the on-going matching and accumulation of information about people, places, businesses, artefacts and even conceptual categories and like 'race' or 'class' to be drawn together, however the subsequent user determines, at the scale of the World Wide Web (Halford, Pope and Weal 2012).However, whilst SLD offers great promise to the social sciences there is - to date - negligible use of SLD by social scientists. The agenda for SLD is being driven by computer scientists and demonstrations are based on relatively straightforward examples such as transport timetables or estates data. Whilst these work well technically, they offer no substantial investigation of how appropriate the techniques might be in addressing more complex social science questions. At a time of financial constraint, when funding for major new data collection is uncertain, it is essential that we explore these opportunities. The research proposed here will be the detailed investigation into if and how SLD might be harnessed for social science research. To achieve this we have drawn together a strong team of social and computational scientists, with a well-established track record of collaboration. This team will be supported by an outstanding Advisory Group of experts, who have already agreed to participate in this project (see Impact Summary below). We will explore three research questions: (i) What are the implications of using SLD methods to describe social data? (ii) What does SLD contribute to our capacity to understand health inequalities across the life course?(iii) What are the implications of SLD for data archiving and re-use? To answer these questions we will: (i) Carry out a detailed study of the processes involved in converting existing data into SLD. This will be undertaken by the research team, with the participation of experts from our Advisory Group. Specifically, we will work with the English Longitudinal Survey of Ageing and the Great British Class Survey and other related data already in the 'linked data cloud'(ii) Develop a 'demonstrator' of SLD (using the data sets developed at (i) above) to examine the specific question of health inequalities across the life-course (iii) Collaborate with the UK Data Service and the GESIS-Leibniz Institute in Germany (which provides a similar data infrastructure to UKDS) to explore the opportunities for data archiving. In this way, we seek to engage social science in the ongoing development of SLD and the emerging Semantic Web; and to explore the implications of SLD for building next generation data infrastructures in the social sciences.

近年来，可用于社会研究的数字数据的数量和范围显着增长。 ESRC 已经投资于利用行政和商业数据以及社会科学的“新的和新兴的数据形式”（例如社交媒体和传感器数据）。现在出现了一个新的机会。 “语义关联数据”（SLD）提供了一种构建和组织数字数据的新方法，它有望以迄今为止难以想象的速度和规模对跨多个异构源的数据链接和分析的研究能力产生深远的影响。事实上，在计算机科学领域，SLD 的支持者认为，如果数据按照共享标准和协议发布，网络将从文档库转变为单一链接数据库，称为“语义网”。数据链接的价值在社会科学中已经得到很好的确立，但现有方法是劳动密集型的，涉及少量数据集的记录的回顾性匹配，并且是为了解决特定的预先确定的问题以及为特定目的而建立的链接。相比之下，SLD 技术侧重于数据的预期生产，以允许在万维网的规模上持续匹配和积累关于人、地点、企业、文物甚至概念类别的信息，例如“种族”或“阶级”，无论后续用户如何决定（Halford、Pope 和 Weal 2012）。然而，尽管 SLD 为社会科学提供了巨大的希望，但迄今为止，社会科学家对 SLD 的使用可以忽略不计。 SLD 的议程由计算机科学家推动，演示基于相对简单的示例，例如交通时间表或房地产数据。虽然这些技术在技术上运作良好，但它们没有对这些技术在解决更复杂的社会科学问题时是否合适进行实质性调查。在财政紧张的时期，当重大新数据收集的资金不确定时，我们有必要探索这些机会。这里提出的研究将详细调查是否以及如何利用 SLD 进行社会科学研究。为了实现这一目标，我们组建了一支强大的社会和计算科学家团队，并拥有良好的合作记录。该团队将得到杰出专家咨询小组的支持，他们已同意参与该项目（请参阅下面的影响摘要）。我们将探讨三个研究问题：（i）使用 SLD 方法描述社会数据有何影响？ (ii) SLD 对我们理解生命全程健康不平等的能力有何贡献？(iii) SLD 对数据存档和再利用有何影响？为了回答这些问题，我们将： (i) 对将现有数据转换为 SLD 所涉及的过程进行详细研究。这将由研究团队在我们顾问小组的专家的参与下进行。具体来说，我们将利用英国老龄化纵向调查和英国阶级调查以及“链接数据云”中已有的其他相关数据(ii) 开发 SLD 的“演示器”（使用上述 (i) 中开发的数据集）来检查整个生命过程中健康不平等的具体问题 (iii) 与英国数据服务和德国 GESIS-莱布尼茨研究所（提供与 UKDS 类似的数据基础设施）合作，探索数据归档的机会。通过这种方式，我们寻求将社会科学纳入 SLD 和新兴语义网的持续发展中；并探讨 SLD 对社会科学领域下一代数据基础设施建设的影响。