# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 2 Related Work
### Ontology Visualization and Ontology-Based Visualization  
Ontologies are used to represent knowledge in complex models and infer information from them, because they allow providing a high semantic content to the data representation. Since the structure of an ontology can be quite complex, many of the works dealing with ontology visualization focus on representing the structure and content of ontologies by means of graphs, trees, Euler diagrams and similar representations [42; 43; 44; 45; 46]. Tools such as OntoViz [47], OWLGrEd [48] or OWLViz [49] can be used to navigate through the ontology and visualize the classes, attributes and rules of the ontology. Nevertheless, ontologies can also play a different and possibly much more important role in the visualization of data. They can be used to define how to visualize data, as proposed in [50; 51]. This research field is called Ontology-based Visualization (OBV), Semantic Visualization (SV) or Knowledge-assisted Visualization (KAV), and the tools that rely on this technology are often called Knowledge-assisted Visualization Tools (KAVT or KVT). In order to distinguish between the different ontologies involved in the process, they receive different names. The ontology used to store the data of a specific domain is called the Domain Ontology (DO). It stores the data that are necessary to be visualized. The ontology used to capture the semantics of a visual representation and store the mapping between each concept and the corresponding visual elements is called the Visual Representation Ontology (VRO), sometimes also referred to as Visualization Ontology. In some occasions, it is also possible to have a third ontology that specifies the relationship between the concepts of the DO and those in the VRO. This is called the Semantic Bridge Ontology (SBO) [52]. One of the earliest works in the KAV field is the work of [PERSON] et al. [53]. They designed an algorithm for suggesting visualizations for semantic data. A similar approach is presented in [54], where the authors investigated strategies to recommend visualizations considering different user preferences and proposed VizRec, a visual recommender tool. The research team involved in [53] is also responsible for the creation of the Visualization Ontology (VISO) [55]. This is a generic and reusable ontology that formalizes knowledge from the visualization domain. This formalization allows making this visualization-related knowledge usable in different contexts. VISO acts as a VRO. The use of VISO alongside the RDFS/OWL Visualization Language (RVL) [56] defines how to visualize the information of another ontology (a DO) and how to interact with it using a user-defined graphical environment. VISO is a generic approach; however, it presents some limitations. First, it is designed in close consideration of bi-dimensional data and hardly delves into three-dimensional visualization elements. In addition, the use of RVL is cumbersome, as it requires tagging the DO in order for VISO to be able to map data into visualizing elements. Although the solution is elegant and generic, it is also not very practical, as it requires modifying the DO. A different approach is performed in [57]. In this work, the authors propose VUMO (a Visualization-oriented Urban Mobility Ontology). VUMO is oriented to visualize data from the urban mobility domain. It is based on concepts from this specific domain, but it also uses the visualization concept class. This class represents many basic geometric objects, from abstract conceptualization to concrete visualization aspects. Finally, it is also possible to use an ontology for both data exploitation and visualization in a particular context. This approach is performed in [27; 57]. As can be seen, the role of ontologies in InfoVis is promising, despite being a recent research area. Ontologies have also demonstrated to be very convenient for showing geographical data, as shown in [58; 59; 60].