ONYX - User Interfaces for Assisting in Interactive Task Learning for Natural Language Interfaces of Data Visualization Tools

Ruoff, Marcel; Myers, Brad A.; Maedche, Alexander

doi:10.1145/3491101.3519793

Cited by 4 publications

(23 citation statements)

References 24 publications

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“…In the 20 articles identified by our search strategy (Section 3.3), we included 10 articles that are chatbot-based [63,[80][81][82][83]86,87,89,91,92] and 10 articles that are form-based NLIs but have some chatbot characteristics such as providing feedback [15,39,40,62,64,69,70,84,88,90].…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 summarises the analysed research works describing the explored data through visualisation: movies, sports, coronavirus, finance and others. We found that 70% (14/20) of them used multidimensional tabular data [39,40,64,[81][82][83][85][86][87][88][89][90][91][92], while some of them also included spatial data [39,70,90,91]. Moreover, the table details the kind of data attributes each V-NLI supports (nominal, numerical, temporal, spatial).…”

Section: Data Spacementioning

confidence: 99%

“…Additionally, Chat2Vis [83] includes a box plot, Talk2Data [64] has a pie chart and Gamebot [86] offers users game shot charts about football and basketball games, as well as displays games statistics in tables. Moreover, some V-NLIs include a map chart, while [39,90,91] have 2D maps in addition to the most popular methods, and [70] includes a 3D map. There are five V-NLIs that have only one visualisation method: GeCoAgent [87] has a pie chart, Ava [81] has a line chart, Valetto [92] and Iris [89] have scatter plots, and DataBreeze [85] uses dots to visualise every data point individually.…”

Section: Visual Spacementioning

confidence: 99%

“…Meanwhile, only 10% (2/20) of them support user-defined mapping [81,89], while 20% (4/20) use only the rule-based visual mappings [40,64,82,86] method. Finally, there are V-NLIs that support a combination of two visual mappings [39,83,88,91].…”

Section: Visual Spacementioning

confidence: 99%

“…For example, InChorus [88] uses rule-based mapping to automatically select a visualisation depending on the attribute type detected from users' queries and it also allows users to explicitly request a visualisation method. Onyx [91] uses fixed visualisation methods but users can change these methods using WIMP or NL. Moreover, Ref.…”

Section: Visual Spacementioning

confidence: 99%

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Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

2023

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Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.

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