# Interactive 3D Exploration of RDF Graphs through Semantic Planes
## 2 Tarsier: Splitting Data among Semantic Planes
### Features  
Tarsier is characterized by a set of features summarized in the following list.  
**Initial Knowledge Base**: : Tarsier is a visualization tool for RDF graphs. As such, it allows to view the whole content of a graph through the canvas. However, RDF graphs may be very large, hosting a high number of triples too difficult to represent in an effective way. For this reason, Tarsier supports a pre-filtering of the knowledge base through SPARQL Construct queries (addressing in this way the requirement \(p0\)) that can be also loaded from a file. In this way, the user may dominate the complexity of the underlying knowledge base focusing only on the information considered relevant for the current task. An example is shown in Figure 2.  
Figure 1: Software architecture of Tarsier. Implementation details are reported with the italic font.  
**Support for RDFS and OWL**: : Despite being ontology agnostic (to adapt to different use cases, as suggested by point \(p5\)), Tarsier, through its data extractor (see Section 2.5), is able to detect classes, datatype properties and object properties through the use of RDFS and OWL constructs (requirement \(p4\)). Comments and labels are also retrieved with the specific predicates.
**Visualization techniques**: : Being able to quickly distinguish classes from other resources, datatype from object properties and rdf:type relationships above all may significantly speed up the analysis process (Figure 3a). Therefore, Tarsier adopts a classification algorithm based (mostly) on a set of SPARQL queries (detailed in Section 2.5) to identify classes, instances of classes, blank nodes, object and datatype properties and rdf:type relationships and paint each of them with a different color. Furthermore, a smart placement algorithm allows facing requirement \(p1\), representing items through the following scheme: classes and individuals are represented as equidistant spherical meshes on two different circumferences. The circle dedicated to classes is the innermost, since usually the number of concepts is less than the number of instances. Blank nodes lay on a third circumference. Datatype properties of an instance are equidistant spheres placed on a circumference centered on the instance (Figure 3c). Further research will be carried out to design and test different arrangement methods.
**Filtering**: : The filtering mechanism (Figure 4) implemented by [PERSON] allows selecting items through UI or SPARQL queries and decide what action to perform. Selection can be related to classes, instances, datatype or object properties as well as literals, URIs or blank nodes. The action  
Figure 3: The classification of RDF terms among blank nodes, individuals, classes or literals as well as data and object properties, bound to using colors provide a more intuitive visualization (**a**), if compared to a monochrome one (**b**), the drawing strategy adopted by [PERSON] (**c**).  
Figure 2: RDF graphs can host a number of triples too high to be effectively and efficiently visualized (**a**), but a prefiltering stage can help to visualize only a subgraph of interest (**b**).  
consists in showing or hiding selected meshes as well as moving them across layers. Every filter applies to the current visualization, allowing incremental filtering. This mechanism allows iteratively building a visualization that fits the user need even for novice users. [PERSON]'s filtering mechanism meets the requirements identified in points \(p2\) and \(p3\).