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.

澳大利亚和其他原住民族面临的迫切问题之一是将其祖先的遗体遣返到他们的社区，期望应用文本挖掘技术和机器学习方法自动化地发现相关文本以协助确认偷窃、捐赠、销售或交换的情况，研究人员确定了关键词，通过将这些关键词与专家植被研究的概念进行匹配以增强模型，模型有效地识别了与原住民人类遗骸全球商业贸易调查相关的文档，并可推广到其他社会科学和人文学科的大型文本语料库中的信息提取。