Question Answering with Deep Neural Networks for Semi-Structured
  Heterogeneous Genealogical Knowledge Graphs
Omri Suissa Maayan Zhitomirsky-Geffet Avshalom Elmalech
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.