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

近年来，可用于社会研究的数字数据的数量和范围都有了惊人的增长。ESRC已经投资于利用行政和商业数据以及社会科学的“新兴数据形式”（例如社交媒体和传感器数据）。现在新的机会出现了。语义链接数据（SLD）提供了一种新的方法来构建和组织数字数据，它有望以迄今难以想象的速度和规模对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，在计算机科学中，SLD的支持者认为，如果数据发布遵循共享的标准和协议，Web将从文档库转变为单个链接的数据库，称为“语义Web”。数据联系的价值已经在社会科学中得到充分确认，但现有方法是劳动密集型的，涉及对少量数据集的记录进行追溯性匹配，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技术专注于数据的预期产生，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、人工制品甚至概念类别的信息，如“种族”或“阶级”，以将其绘制在一起，然而随后的用户确定然而，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。虽然这些技术在技术上运作良好，但它们并没有对这些技术在解决更复杂的社会科学问题时的适用性进行实质性的研究。在财政紧张的时候，当主要新数据收集的资金不确定时，我们必须探索这些机会。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，我们召集了一支强大的社会和计算科学家团队，他们拥有良好的合作记录。该小组将得到一个杰出的专家咨询小组的支持，这些专家已同意参加该项目（见下文影响摘要）。我们将探讨三个研究问题：（i）使用SLD方法来描述社会数据的含义是什么？(ii)SLD对我们理解整个生命过程中健康不平等的能力有什么贡献？(iii)SLD对数据归档和重复使用有什么影响？为了回答这些问题，我们将：（i）对将现有数据转换为SLD的过程进行详细研究。这项工作将由研究小组进行，咨询小组的专家将参加。具体地说，我们将与英国老龄化纵向调查和大不列颠阶级调查以及已经在“关联数据云”中的其他相关数据合作。（ii）开发SLD的“演示器”（使用上文（一）中开发的数据集）审查整个生命过程中健康不平等的具体问题（三）与联合王国数据服务处和德国的GESIS-Leibniz研究所合作（提供与UKDS类似的数据基础设施），以探索数据存档的机会。通过这种方式，我们寻求从事社会科学的持续发展的SLD和新兴的语义网，并探讨SLD的影响，在社会科学中建立下一代数据基础设施。

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.