  Knowledge graphs (KG) have become an important data organization paradigm.
The available textual query languages for information retrieval from KGs, as
SPARQL for RDF-structured data, do not provide means for involving
non-technical experts in the data access process. Visual query formalisms,
alongside form-based and natural language-based ones, offer means for easing
user involvement in the data querying process. ViziQuer is a visual query
notation and tool offering visual diagrammatic means for describing rich data
queries, involving optional and negation constructs, as well as aggregation and
subqueries. In this paper we review the visual ViziQuer notation from the
end-user point of view and describe the conceptual and technical solutions
(including abstract syntax model, followed by a generation model for textual
queries) that allow mapping of the visual diagrammatic query notation into the
textual SPARQL language, thus enabling the execution of rich visual queries
over the actual knowledge graphs. The described solutions demonstrate the
viability of the model-based approach in translating complex visual notation
into a complex textual one; they serve as semantics by implementation
description of the ViziQuer language and provide building blocks for further
services in the ViziQuer tool context.
