  With the growing number and size of Linked Data datasets, it is crucial to
make the data accessible and useful for users without knowledge of formal query
languages. Two approaches towards this goal are knowledge graph visualization
and natural language interfaces. Here, we investigate specifically question
answering (QA) over Linked Data by comparing a diagrammatic visual approach
with existing natural language-based systems. Given a QA benchmark (QALD7), we
evaluate a visual method which is based on iteratively creating diagrams until
the answer is found, against four QA systems that have natural language queries
as input. Besides other benefits, the visual approach provides higher
performance, but also requires more manual input. The results indicate that the
methods can be used complementary, and that such a combination has a large
positive impact on QA performance, and also facilitates additional features
such as data exploration.
