The Congruence Engine: Digital Tools for New Collections-Based Industrial Histories

在历史物品和来源之间建立牢固联系的能力是这个项目的核心，就像它在日常博物馆和旨在支持的历史实践中所做的那样。策展人将文物、图像、视听材料和历史结合在一起。家族和当地历史学家将祖先和地方的记录联系起来，以建立他们的家谱或了解他们居住的过去。学术历史学家耐心地、批判性地将各种档案来源与现有文献联系起来，讲述关于过去的新故事。它们都依赖于连接过去的不同片段，因为它们创造了构成我们地方和国家历史的叙事挂毯。Concruence引擎将为每个对过去着迷的人创建一个数字工具箱的原型，将国家收藏中前所未有的一系列物品连接起来，讲述他们想要讲述的关于我们工业历史的故事。到目前为止，我们已经适应了一个研究的世界，在这个世界里，我们只能为我们想要进行的任何历史调查选择可能相关的历史来源材料。现在，在我们的信息社会，我们希望去搜索引擎，找到任何东西的记录。但这样的搜索往往令人失望，主要有两个原因。首先是因为自由文本搜索的专制，其中排名的结果列表偏爱以前搜索的结果，并且不能保证包括与搜索相关的全部内容。第二个原因是，我们的许多遗产收藏品的记录都很薄、不一致，或者保存在机构的竖井中，不让外界访问。此项目显式使用通常由弱数据表示的这些集合。代替二维排名的搜索引擎列表，我们的目标是建立一个世界的模型，在这个世界中，用户将能够探索数据邻域(从技术上讲，是“知识图谱”)，在那里，与他们的调查密切相关的关于遗产项目的大量信息将很容易被手工获取-博物馆文物、档案文件、图片、电影、建筑，以及以前调查和相关活动的记录。在“遗产连接器”(科学馆的TANC基金会项目)成功试验的基础上，这一重大项目将开发一套原型发现工具，以获取我们的调查和合作组织和合作伙伴带来的工业和相关收藏品。为了实现藏品可获得性方面的这一突破，它将通过合作将技能和兴趣的独特组合聚集在一起。在这里，数字研究人员将与专业和社区历史学家和策展人合作，解决对英国工业历史的真实世界历史调查。通过对纺织、能源和通信三个工业领域长达27个月的迭代探索，数字研究人员将与历史学家和策展人合作，调整软件，使其能够响应用户需求。他们将相应地使用计算和人工智能技术--包括机器学习和自然语言处理(特别是命名实体识别)和一套定制的实体链接例程--来创建和改进数据集，提供记录与扫描和照片等数字对象之间的路径，并创建工具，使参与者--他们不需要是数字专家--能够享受和利用在构建叙事时向他们开放的资源。这些叙述将在该项目的移动数字展览空间、其网站和各种传统的流行和学术成果中表达出来。软件将通过GitHub提供；我们将制作《如何操作》指南。

The capacity to make strong connections between historical objects and sources lies at the heart of this project as it does in the everyday museum and historical practices that it is designed to support. Curators creating displays combine artefacts, images, audio-visual materials and histories. Family and local historians connect records of ancestors and localities to establish their genealogy or to understand the past of where they live. Academic historians patiently and critically connect a diverse range of archive sources with existing literature to tell new stories about the past. All rely on connecting different fragments of the past as they create the tapestries of narrative that constitute our local and national histories. The Congruence Engine will create the prototype of a digital toolbox for everyone fascinated by the past to connect an unprecedented range of items from the nation's collection to tell the stories about our industrial past that they want to tell. Until now, we have become acclimatised to a world of research where it has only been possible to work with a selection of the potentially relevant historical source material for any historical investigation we want to undertake. And now, in our information society, we expect to go to a search engine and find a record of anything. But so often such searches disappoint, and for two main reasons. First because the tyranny of the free-text search where ranked results lists favour the results of previous searches, and cannot be guaranteed to include the full set of what is relevant to the search. The second reason is that the records of so very many of our heritage collections are thin, inconsistent, or kept in institutional siloes hidden from outside access. This project explicitly works with these collections that are generally represented by weak data. In place of the two-dimensional ranked list of search engines, we aim, with 'The Congruence Engine', to model a world in which users will be able to explore data neighbourhoods (technically 'knowledge graphs') where a great diversity of information about heritage items that are deeply relevant to their investigations will be readily to hand - museum objects, archive documents, pictures, films, buildings, and the records of previous investigations and relevant activity. Building on the successful experimentation of 'Heritage Connector' (the Science Museum's TaNC foundation project), this major project will develop a repertoire of prototype discovery tools to access the industrial and related collections brought into the study from our investigating and collaborating organisations and partners. To achieve this breakthrough in collections accessibility, it will bring together in collaboration a unique combination of skills and interests. Here, digital researchers will work with professional and community historians and curators to address real-world historical investigations of Britain's industrial past. Through 27 months of iterative exploration of three industrial sectors - textiles, energy and communications - the digital researchers will work with the historians and curators, tuning the software to make it responsive to user needs. They will responsively use computational and artificial intelligence techniques - including machine learning and natural language processing (specifically, eg, named entity recognition) and a suite of bespoke entity-linking routines - to create and refine datasets, provide routes between records and digital objects such as scans and photographs, and create the tools by which the participants - who will not need to be digital experts - will be able to enjoy and employ the sources that are opened to them in the construction of narratives. These narratives will be expressed in the project's mobile digital exhibition space, on its website and a variety of conventional popular and academic outputs. Software will be made available via GitHub; we will produce 'how to' guides.