  Structured and unstructured data and facts about drugs, genes, protein,
viruses, and their mechanism are spread across a huge number of scientific
articles. These articles are a large-scale knowledge source and can have a huge
impact on disseminating knowledge about the mechanisms of certain biological
processes. A knowledge graph (KG) can be constructed by integrating such facts
and data and be used for data integration, exploration, and federated queries.
However, exploration and querying large-scale KGs is tedious for certain groups
of users due to a lack of knowledge about underlying data assets or semantic
technologies. A question-answering (QA) system allows the answer of natural
language questions over KGs automatically using triples contained in a KG.
Recently, the use and adaption of digital assistants are getting wider owing to
their capability at enabling users to voice commands to control smart systems
or devices. This paper is about using Amazon Alexa's voice-enabled interface
for QA over KGs. As a proof-of-concept, we use the well-known DisgeNET KG,
which contains knowledge covering 1.13 million gene-disease associations
between 21,671 genes and 30,170 diseases, disorders, and clinical or abnormal
human phenotypes. Our study shows how Alex could be of help to find facts about
certain biological entities from large-scale knowledge bases.
