Humans have a curious nature and seek a better understanding of the world. Data, in-formation, and knowledge became assets of our modern society through the informationtechnology revolution in the form of the internet. However, with the growing size ofaccumulated data, new challenges emerge, such as searching and navigating in these largecollections of data, information, and knowledge. The current developments in academicand industrial contexts target the corresponding challenges using Semantic Web techno-logies. The Semantic Web is an extension of the Web and provides machine-readablerepresentations of knowledge for various domains. These machine-readable representationsallow intelligent machine agents to understand the meaning of the data and information;and enable additional inference of new knowledge.Generally, the Semantic Web is designed for information exchange and its processingand does not focus on presenting such semantically enriched data to humans. Visualizationssupport exploration, navigation, and understanding of data by exploiting humans’ abilityto comprehend complex data through visual representations. In the context of Semantic-Web-Based knowledge structures, various visualization methods and tools are available,and new ones are being developed every year. However, suitable visualizations are highlydependent on individual use cases and targeted user groups.In this thesis, we investigate visual exploration techniques for Semantic-Web-Basedknowledge structures by addressing the following challenges: i) how to engage various usergroups in modeling such semantic representations; ii) how to facilitate understanding usingcustomizable visual representations; and iii) how to ease the creation of visualizationsfor various data sources and different use cases. The achieved results indicate that visualmodeling techniques facilitate the engagement of various user groups in ontology modeling.Customizable visualizations enable users to adjust visualizations to the current needs andprovide different views on the data. Additionally, customizable visualization pipelinesenable rapid visualization generation for various use cases, data sources, and user groups