# Interactive 3D Exploration of RDF Graphs through Semantic Planes
## 3 Use Cases
### Use Case #2: Exploring DBpedia
Moreover, a help screen was added to further guide a new user to the tool and three introductory

Figure 16: Use Case #3: A first semantic plane hosts instances of the class rdf:Statement (**a**). A multi-planar view with a topmost semantic plane dedicated to foaf:Organization1 shows the link among the organization and the statements (**b**). Then, unnecessary edges are hidden (**c**). videoclips (available on the GitHub page: [[https://github.com/desmovalvo/tarsier](https://github.com/desmovalvo/tarsier)]([https://github.com/desmovalvo/tarsier](https://github.com/desmovalvo/tarsier))) showcase it through three examples based on the three use cases proposed in this paper. ### Performance Evaluation

Even though performance is not a primary aspect of inspection tools including Tarsier, this section presents the results of preliminary evaluation tests. Intuitively, the time needed to draw the graph depends on three main factors:

* the amount of data to be traced (i.e., the number of meshes);
* the requested level of detail that is one of the parameters configurable by the user; and
* wether the 3D scene has been initialized (i.e., the latter condition is defined _cold start_). A generic dataset proposed with five different sizes was utilized. Datasets are identified by labels DS# with \(i=1,\ldots,5\). Dataset DS#\(i\) contains \(200\cdot i\) triples, and the full representation requires drawing \(200\cdot i+1\) spherical meshes and \(200\cdot i\) bezier curves (adopted for datatype and object properties). For the purpose of this test, the specific content of the knowledge base does not influence the evaluation. Every dataset was tested with both a cold start condition and with an already initialized scene. Four LOD values were tested (4, 8, 12 and 16). All tests were performed on a Dell Alienware with 8-core Intel(R) Core(TM) i7-4720 HQ CPU at 2.60 GHz and 8 GB RAM. Both server and client were running on the same machine using Google Chrome 64.0. Figures 17(a), 18(a), 18(a), 18(a) and 18(a) report the results of cold start test, while Figures 17(b), 18(b), 18(b), 18(b) and 18(b) report the results of tests executed on an instance of Tarsier where the scene was already initialized. The charts confirm the expected behavior of the application: time needed to draw the graph grows with the size of data and with the requested level of detail. Furthermore, a cold start of Tarsier (i.e., where the scene is not yet initialized) requires a higher number of milliseconds to complete the drawing. Lastly, Figure 23 compares the time needed to identify the role of each RDF term in the knowledge base for the five different datasets. Figure 17: Mean and standard error of the mean (SEM) of the results of the questionnaire items. Figure 19: Time to represent DS#2. Figure 18: Time to represent DS#1. Figure 21: Time to represent DS#4. Figure 20: Time to represent DS#3. ## 5 Related Work

Graph theory [14] and the use of graph data structures have found their application in several domains, from chemistry and biology, to social sciences, networks and, of course, the Semantic Web [1]. In particular, ontologies and Linked Data take the form of oriented and labeled graphs and so the tools for their interactive visualization could be classified as: (1) tools for the visualization and exploration of all kinds of graphs with the support of plugins/extensions to import semantic data; and (2) tools

Figure 14:0 Time employed by the data extractor to analyze data depending on the dataset. Figure 14:1 Time to represent DS#5. specifically designed for the Semantic Web. In general, the former offer sophisticated analysis and exploration features for graphs expert users, while the latter implement a reduced set of functions but are more suitable for Semantic Web users. While in the rest of this section we focus on the use of graphs, we would like to mention that this is not the only approach to RDF data visualization, as demonstrated by [PERSON] et al. (2015) who proposed for example a method to visualize RDF data based on a 3D adjacency matrix.