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

一致性引擎：基于新馆藏的工业历史的数字工具

• 批准号: AH/W003244/1
• 负责人: Timothy Boon
• 金额: $ 374.86万
• 依托单位: Science Museum Group
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FW003244%2F1
• 关键词: Congruence; Engine; Digital; Tools; New

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.

建立历史对象和资源之间建立牢固联系的能力是该项目的核心，就像在日常博物馆和旨在支持的历史实践中一样。策展人创建显示的组合，图像，视听材料和历史。家庭和地方历史学家将祖先和地方的记录联系起来，以建立家谱或了解他们居住的过去。学术历史学家耐心和严格地将各种档案资源与现有文献联系起来，讲述有关过去的新故事。所有人都依靠将过去的不同碎片连接起来，因为它们创建了构成我们当地和民族历史的叙事挂毯。一致性引擎将为过去的每个人都创建数字工具箱的原型，以连接美国藏品的前所未有的物品范围，以讲述他们想要讲的有关我们工业过去的故事。到目前为止，我们已经适应了一个研究世界，在这种研究世界中，只有可以选择我们想要进行的任何历史调查的潜在相关历史原理材料。现在，在我们的信息社会中，我们希望去搜索引擎并找到任何事物的记录。但是，这种搜索常常令人失望，这是两个主要原因。首先是因为自由文本搜索的暴政列出了排名的结果列出了以前的搜索结果，并且不能保证包括与搜索相关的完整集。第二个原因是，我们许多遗产收藏的记录很薄，不一致，或者保存在从外部通道中隐藏的机构孤岛中。该项目明确地与通常由弱数据表示的这些收藏品合作。 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.在成功的“遗产连接器”（科学博物馆的塔克基金会项目）的成功实验的基础上，这个主要项目将开发一个原型发现工具的曲目，以获取我们调查和协作组织和合作伙伴从我们的研究中带来的工业和相关收藏。为了实现收藏可访问性的突破，它将共同融合技能和兴趣的独特结合。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，以解决对英国工业过去的现实历史调查。在对三个工业领域（纺织品，能源和通信）的27个月迭代探索中，数字研究人员将与历史学家和策展人合作，调整软件以使其能够满足用户需求。他们将响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，例如，例如，命名实体识别）以及一套定制的实体链接常规 - 来创建和完善数据集，提供记录和数字对象之间的途径，并在扫描和照片中提供数字构建，并享受这些参与者的创建，以使他们能够开放的工具 - 这些工具 - 可以促成数字专家 - 既可以供应了，又可以满足他们的能力，而这些工具 - 叙事。这些叙述将在该项目的移动数字展览空间，其网站以及各种传统的流行和学术成果上表达。软件将通过GitHub提供；我们将产生“如何”指南。

项目成果

History of communications and the Congruence Engine: early thoughts and possibilities

通信史和同余引擎：早期思想和可能性

• DOI: 10.15180/221806
• 发表时间: 2023
• 期刊: Science Museum Group Journal
• 作者: Agar J

History of textiles and the Congruence Engine

纺织品的历史和同余引擎

• DOI: 10.15180/221815
• 发表时间: 2023
• 期刊: Science Museum Group Journal
• 作者: Ashworth W

The future: reflections on emerging machine-learning methods for digital heritage

未来：对数字遗产新兴机器学习方法的思考

• DOI: 10.15180/221818
• 发表时间: 2023
• 期刊: Science Museum Group Journal
• 作者: Calow A

Connecting with industrial heritage collections using video production methods: Greg Kotovs and the can-gill machine

使用视频制作方法连接工业遗产收藏：Greg Kotovs 和罐刺机

• DOI: 10.15180/221808
• 发表时间: 2023
• 期刊: Science Museum Group Journal
• 作者: Craddock P

Congruence Engine in action – an emergent editorial

运行中的一致性引擎

• DOI: 10.15180/221803
• 发表时间: 2023
• 期刊: Science Museum Group Journal
• 作者: Graham H