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

近年来，可用于社会研究的数字数据在数量和范围上都出现了惊人的增长。ESRC已经投资于利用行政和商业数据以及“新兴的数据形式”（例如社交媒体和传感器数据）为社会科学服务。现在，一个新的机会出现了。“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，有望以迄今难以想象的速度和规模，对跨多个异构来源的数据链接和分析的研究能力产生深远影响。事实上，在计算机科学中，SLD的支持者认为，如果数据按照共享的标准和协议发布，那么网络将从文档库转变为一个单一的链接数据库，称为“语义网”。在社会科学中，数据联系的价值已经得到很好的确立，但是现有的方法是劳动密集型的，涉及在少数数据集上对记录进行回顾性匹配，并且是为了解决特定的预先确定的问题，并为此特定目的建立联系。相比之下，SLD技术侧重于数据的前瞻性生产，以允许持续匹配和积累有关人物、地点、企业、人工制品甚至概念类别的信息，如“种族”或“阶级”，无论后续用户如何确定，都可以在万维网的规模上进行匹配和积累（Halford, Pope and 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的使用微不足道。SLD的议程是由计算机科学家推动的，演示是基于相对简单的例子，如运输时间表或房地产数据。虽然这些方法在技术上很有效，但它们并没有提供关于这些技术在解决更复杂的社会科学问题时是否合适的实质性调查。在财政紧张的情况下，在主要新数据收集的资金不确定的情况下，我们必须探索这些机会。这里提出的研究将是关于是否以及如何利用特殊语言障碍进行社会科学研究的详细调查。为了实现这一目标，我们汇集了一支强大的社会和计算科学家团队，他们有着良好的合作记录。该小组将得到一个杰出的专家咨询小组的支持，他们已经同意参加这个项目（见下面的影响摘要）。我们将探讨三个研究问题：(i)使用SLD方法描述社会数据的含义是什么？（二）特殊学习能力对我们理解整个生命过程中健康不平等的能力有何帮助？（iii） SLD对数据存档和再使用有何影响？为了回答这些问题，我们将：(i)详细研究将现有数据转换为可持续发展数据的过程。这项工作将由研究小组进行，咨询小组的专家将参与。具体地说,我们将与英国老龄化纵向调查和英国阶级调查以及“关联数据云”中已有的其他相关数据合作（ii）开发SLD的“示范”（使用上文(i)开发的数据集），以检查整个生命过程中健康不平等的具体问题（iii）与英国数据服务处和德国gesis -莱布尼茨研究所合作（该研究所为英国数据服务处提供类似的数据基础设施），以探索数据的机会归档。通过这种方式，我们寻求让社会科学参与到SLD和新兴语义网的持续发展中；并探索SLD对构建社会科学领域下一代数据基础设施的影响。

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.