# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 5 Discussion
### RQ1: How Do Chatbot-Based V-NLIs Contribute to Interactions with the Data Space? To answer this research question, we contrasted the results of the input characteristics of V-NLIs (Table 4) with how these systems deal with the data space stage in the visualisation pipeline (Table 1). We found that most of the works allow the users to express only low-level queries, and those that consider high-level queries do so with simple data types (see Figure 10, signal a and b) and attributes, i.e., tabular data with numerical and nominal attributes. Therefore, there is a gap in the use of natural language for the analysis of complex data (network and hierarchical) and also in the use of spatial and temporal attributes. This gap can be due to two reasons. **Remark 1**. : _We suggest designing V-NLIs considering complex data using high-level queries and extending their study to Post-WIMP interfaces, i.e., the so-called immersive analytics in VR and AR._  
First, low-level queries may make it difficult for users to perform visual analytic tasks with complex data (e.g., analysing subgraphs in network visualisations). Actually, the use of NLP to elaborate high-level queries on this type of data has limitations on both sides. On one hand, users need to express their intents. On the other hand, the NLP understanding system has to deal with ambiguities. Indeed, Talk2 Data [64] and Chat2 Vis [83] are the only reviewed works that used high-level queries, both with tabular data. However, the former has a form-based interface, and although the latter is chatbot-based, it lacks chatbot qualities such as conversational feedback and viewing conversation history. In this context, some recent approaches attempted to split NLI high-level intents directly into nested SQL-queries [105; 106]. Second, complex data are usually projected into a two-dimensional space, hindering queries about complex structures, such as multivariate hierarchical and network data, which would be better queried in a three-dimensional space [107]. Therefore, we suggest designing V-NLIs considering high-level queries as well as extending their study beyond WIMP interfaces, i.e., the so-called immersive analytics in VR and AR [108]. Figure 10: Spider chart displaying the relationship between data types and input V-NLI characteristics. Moreover, independently of the user's intents (low or high queries), all the examined V-NLIs contemplate simple data transformations (i.e., simple aggregations and statistical analysis such as correlations and logistic regressions). Note also that those simple data transformations have normally been incorporated into V-NLI systems that consider follow-up queries [39; 40]. The reason can be found in analytical conversations, where this type of query makes it easier for the user to request successive data transformations beyond the initial or current visualisation. In this context, we suggest that V-NLI systems allow users to ask for more complex data transformations, such as visual binning or the extraction of subsets for the analysis of specific parts of the data [30; 109; 110] To do so, we think that combining Natural Language with other interaction styles may help the user to express the context of the visualisation, in line with the proposal of [PERSON] et al. [63], where the user's NL-based queries refer to the part of the visualisation selected with the mouse. For example, suppose there is a 3D scene that shows a hierarchical graph. In this scenario, the user could utilise a VR hand controller to indicate the specific part of the visualisation to which the query refers. This idea would also be useful to indicate the target in focus+context and multi-view visualisations. **Remark 2**: _Combining Natural Language with other interaction styles (VR, AR) may help the user to better express the data queries using the visual context during the conversation._  
Additionally, to let users better express their intents with less ambiguity, V-NLI systems use either guidance strategies or multimodality.