A comparative user evaluation on visual ontology modeling using node-link diagrams

The emergence of several ontology modeling tools is motivated by the growing attention ontologies receive in scientific and industrial contexts. The available tools implement different ontology modeling paradigms, including text-based editors, graphical user interfaces with hierarchical trees and form widgets, and visual modeling approaches based on node-link diagrams. In this paper, we present an empirical user study comparing a visual ontology modeling approach, based on node-link diagrams, with a modeling paradigm that uses hierarchical trees and form widgets. In particular, the user study compares the two ontology modeling tools Protégé and WebVOWL Editor, each implementing one of the modeling paradigms. The involved participants were given tasks of ontology modeling and also answered reflective questions for the individual tools. We recorded the completion times of the modeling tasks and the errors made as well as the usersâ understanding of the conceptual spac es. The study indicates that visual ontology modeling, based on node-link diagrams, is comparatively easy to learn and is recommended especially for users with little experience in ontology modeling and its formalization. For more experienced users, no clear performance differences are found between the two modeling paradigms; both seem to have their pros and cons depending on the type of ontology and modeling context

Visual exploration of semantic-web-based knowledge structures

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 information technology revolution in the form of the internet. However, with the growing size of accumulated data, new challenges emerge, such as searching and navigating in these large collections of data, information, and knowledge. The current developments in academic and industrial contexts target the corresponding challenges using Semantic Web techno- logies. The Semantic Web is an extension of the Web and provides machine-readable representations of knowledge for various domains. These machine-readable representations allow 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 processing and does not focus on presenting such semantically enriched data to humans. Visualizations support exploration, navigation, and understanding of data by exploiting humans’ ability to 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 highly dependent on individual use cases and targeted user groups. In this thesis, we investigate visual exploration techniques for Semantic-Web-Based knowledge structures by addressing the following challenges: i) how to engage various user groups in modeling such semantic representations; ii) how to facilitate understanding using customizable visual representations; and iii) how to ease the creation of visualizations for various data sources and different use cases. The achieved results indicate that visual modeling techniques facilitate the engagement of various user groups in ontology modeling. Customizable visualizations enable users to adjust visualizations to the current needs and provide different views on the data. Additionally, customizable visualization pipelines enable rapid visualization generation for various use cases, data sources, and user group