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.

翻译：知识图谱已成为重要的数据组织范式。现有的从知识图谱中检索信息的文本查询语言（如针对RDF结构数据的SPARQL）无法为非技术专家参与数据访问过程提供支持。基于表单和自然语言的查询形式主义与可视化查询形式主义共同为用户参与数据查询过程提供了便捷途径。ViziQuer是一种可视化查询符号与工具，通过图解化手段描述包含可选与否定结构、聚合及子查询的复杂数据查询。本文从最终用户视角审视ViziQuer可视化符号体系，阐述将可视化图解查询符号映射为文本SPARQL语言的概念与技术方案（包括抽象语法模型及后续的文本查询生成模型），从而实现对真实知识图谱执行复杂可视化查询。所述方案证明了基于模型的方法在将复杂可视化符号转换为复杂文本符号时的可行性；这些方案通过实现描述为ViziQuer语言提供了语义依据，并为ViziQuer工具环境中的后续服务奠定了构建基础。