# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 2 Background
### Visual Space
There is a broad range of graphical elements (also called mark types) used to map attributes, such as points, lines, glyphs, icons and symbols. Some of them are more suitable for displaying quantitative attributes such as points, while others are better suited to nominal data, where a symbol can communicate the meaning of the data in a pictorial way [2; 43]. In this paper, we analyse the related works in terms of a semantic continuum of the graphical elements which goes from the more **abstract** (e.g., points, cross, stars) to the more meaningful or **symbolic** (e.g., glyphs, icons). We also take into account the graphical properties that can enhance one's understanding of the graphical elements, such as colours, size, position, orientation, value, textures, shapes, connectivity, grouping and animation. In addition, as in layout selection, finding adequate graphical elements for a given dataset and its properties is not a trivial task. In general, users can interactively select these graphical elements, although, as in the case of layouts, other methods have been proposed based on expert-defined rules [44] and intelligent algorithms that recommend [45] or infer the elements by means of pre-trained models with the most commonly used graphical elements [46]. Thus, in summary, to analyse the reviewed papers in terms of the **visual mapping identification**, i.e., to choose layouts and graphical elements and properties, we use the following categories: **fixed, user-defined, rule-based** (we refer to basic rule-based methods as those that follow a set of heuristics and make decisions based on them), and **intelligent methods** (intelligent methods involve the use of machine learning, artificial intelligence or other computational techniques to enable systems to learn from data, adapt and make decisions in a more flexible and adaptive manner). Once the visual mapping is performed, the **View Transformation** stage allows users to change the viewpoint (e.g., zooming and panning), perform location probes (to measure values in samples), and create some distortions in the image (i.e., change the projection type) [23]. Additionally, view transformation allows users to take into account multiple views simultaneously, as well as animations and others. Some view transformations emphasise data with importance-driven strategies to enhance values and regions of interest, among other factors. Focus+Context [47] highlights the important data (focus) while the rest of the data provide additional information on the background (context), which allows users to see the details as well as the entire perspective. For example, imagine a line chart showing sales over time in which the peak point is highlighted (focus) but you can still see the all sales over time in the background (context). Other methods use the size of the items to show different levels of detail simultaneously, such as the multi-resolution approach [48], which allows users to select different resolutions to drill down and see details as needed. For example, a treemap exploits multiresolution, showing overall sales of all the continents in the outer rectangles so that the user can select a specific continent to view the details of sales of its countries in nested rectangles. We will describe the reviewed works in terms of the number of views that they use simultaneously **(Single/Multiple)** and the **strategy** used to emphasise regions or parts of the view (zoom, panning, focus+context, level of detail, multiresolution and others). Last but not least is the **Interaction Space** (shown in blue in the upper-right part of Figure 1), where the users interact with all the previous steps defined above. There have been many attempts to categorise different interactions [49,50]. [PERSON] et al. [10] proposed seven interaction methods based on the user's intents: **select**, **explore**, **reconfigure**, **encode**, **abstract/elaborate**, **filter** and **connect**.