  One of the key AI tools for textual corpora exploration is natural language
question-answering (QA). Unlike keyword-based search engines, QA algorithms
receive and process natural language questions and produce precise answers to
these questions, rather than long lists of documents that need to be manually
scanned by the users. State-of-the-art QA algorithms based on DNNs were
successfully employed in various domains. However, QA in the genealogical
domain is still underexplored, while researchers in this field (and other
fields in humanities and social sciences) can highly benefit from the ability
to ask questions in natural language, receive concrete answers and gain
insights hidden within large corpora. While some research has been recently
conducted for factual QA in the genealogical domain, to the best of our
knowledge, there is no previous research on the more challenging task of
numerical aggregation QA (i.e., answering questions combining aggregation
functions, e.g., count, average, max). Numerical aggregation QA is critical for
distant reading and analysis for researchers (and the general public)
interested in investigating cultural heritage domains. Therefore, in this
study, we present a new end-to-end methodology for numerical aggregation QA for
genealogical trees that includes: 1) an automatic method for training dataset
generation; 2) a transformer-based table selection method, and 3) an optimized
transformer-based numerical aggregation QA model. The findings indicate that
the proposed architecture, GLOBE, outperforms the state-of-the-art models and
pipelines by achieving 87% accuracy for this task compared to only 21% by
current state-of-the-art models. This study may have practical implications for
genealogical information centers and museums, making genealogical data research
easy and scalable for experts as well as the general public.
