With the rising popularity of user-generated genealogical family trees, new genealogical information systems have been developed. State-of-the-art natural question answering algorithms use deep neural network (DNN) architecture based on self-attention networks. However, some of these models use sequence-based inputs and are not suitable to work with graph-based structure, while graph-based DNN models rely on high levels of comprehensiveness of knowledge graphs that is nonexistent in the genealogical domain. Moreover, these supervised DNN models require training datasets that are absent in the genealogical domain. This study proposes an end-to-end approach for question answering using genealogical family trees by: 1) representing genealogical data as knowledge graphs, 2) converting them to texts, 3) combining them with unstructured texts, and 4) training a trans-former-based question answering model. To evaluate the need for a dedicated approach, a comparison between the fine-tuned model (Uncle-BERT) trained on the auto-generated genealogical dataset and state-of-the-art question-answering models was per-formed. The findings indicate that there are significant differences between answering genealogical questions and open-domain questions. Moreover, the proposed methodology reduces complexity while increasing accuracy and may have practical implications for genealogical research and real-world projects, making genealogical data accessible to experts as well as the general public.

通过将家族谱系数据表示为知识图谱，转化为文本，将其与非结构化文本相结合，并训练一个基于转换器的问答模型，该研究提出了一种适用于家族谱系数据的问答端到端方法。通过将经过微调的模型（Uncle-BERT）与现有的问答模型进行比较，结果表明回答家族谱系问题和开放领域问题之间存在显著差异。此外，该方法减少了复杂性，提高了准确性，并可能对家族谱系研究和实际项目具有实际意义，使家谱数据对专家和普通公众都更易获取。

使用深度神经网络应对半结构化异构家谱知识图的问答