# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 1 Introduction  
The history of Europe is woven in silk. This material has shaped the way our society is today. However, silk textiles are a seriously endangered heritage, because of their fragility. In addition, several other related intangible cultural heritage assets, such as silk weaving techniques, are also at risk, since traditional looms and craftsmen/crafts women have almost disappeared. For this reason, the European Commission has funded the SILKNOW project [1] under the H2020 framework program. The aim of this project, in which the authors participate, is to produce a computational system that helps understanding and preserving silk cultural heritage. Understanding our cultural heritage (CH) is crucial to appreciate it and protect it. This principle was set up by [PERSON] in 1957: \"Through interpretation, understanding; through understanding, appreciation; through appreciation, protection\" [2]. Indeed, this principle is still valid today, since without a proper understanding of our past, it is impossible to shape a better future. One of the tasks that are needed to understand our heritage is to be able to collect and interpret data about it. To be able to analyze these data, it is crucial to visualize itand do it properly [3; 4; 5]. Currently, massive amounts of information are generated and processed every day. Some of these data could be harnessed and may even lead to scientific discoveries or to the confirmation of scientific concepts/theories. However, understanding first the proper mechanisms to visualize this large amount of information is sometimes as important as the analysis itself, since these scientific discoveries often arise after observing patterns and relationships in the analyzed data by visualizing data, as in [6]. In CH, and in many other areas as well, space and time represent two of the most valuable dimensions. Spatio-temporal datasets, describing the evolution of information with respect to both space and time, are of the utmost importance in scientific analysis. However, visualizing data in space and time, or _spacetime_--following a more accurate physical depiction of these two variables that are, at a fundamental level, inseparable according to [PERSON]'s theory of general relativity--usually require a graphical representation of, at least, four dimensions, which is not easy to build on a two-dimensional computer screen or even in a three-dimensional world. On the other hand, adding semantic information to the data helps identifying patterns and harnessing the knowledge embedded within the data. One of the most common ways of doing this is by creating ontologies. An ontology in computer science \"defines the basic terms and relations comprising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary\" [7]. Ontologies are used to represent complex models of information in which semantic content plays an important role and can be used to infer hidden knowledge from data. However, most visualization tools are standalone applications that handle small datasets and do not use ontologies nor are they capable of visualizing data stored in an ontology. In this regard, the visualization of multidimensional data often leads to ad hoc solutions, in which each particular application offers a different approach for visualizing its data. This unstructured approach can be improved using ontologies, since ontologies can be employed both to store the data that needs to be visualized but also to define how to visualize them. This second use is especially important as it provides a way to systematize the visualization tasks. This systematization is quite complex when the number and type of datasets are unknown, but can be handled if we know that data should be visualized in spatio-temporal terms, as it is the case of most CH applications. This restriction allows addressing the problem in a more attainable approach.