Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.

翻译：澳大利亚及其他原住民族面临的紧迫问题之一是归还其祖先的遗骸，这些遗骸目前保存在西方科研机构中。确保这些遗骸回归其社区进行重新安葬的成功，很大程度上取决于在1790年至1970年间发表的科学及其他文献中定位相关信息——这些文献记录了遗骸的盗窃、捐赠、出售或机构间交换。本文报告了数据科学家与社会科学研究者在“研究、和解与复兴网络”（RRR）中开展的合作研究，旨在开发和应用文本挖掘技术以识别这些关键信息。我们描述了迄今为止在开发基于机器学习解决方案方面的工作，该方案用于自动化查找和语义分析相关文本。分类模型，尤其是基于深度学习的模型，在仅用少量标注（即相关/不相关）文档训练时，已知准确率较低。为提高检测模型的准确性，我们探索使用一种基于专家知情的上下文知识来描述文档内容的知情神经网络（INN）模型。该模型仅使用少量标注文档提供特异性，通过RRR专家在来源研究中识别的概念相关关键词实现。结果证实了使用INN网络模型识别与全球原住民遗骸商业贸易调查相关文档的价值。实证分析表明，该INN模型可推广至社会科学与人文学科的其他研究者，用于从大型文本语料库中提取相关信息。