Experts face the task to decide where and how land reuse---transforming previously used areas into landscape and utility areas---can be performed. This decision is based on which area should be used, which restrictions exist, and which conditions have to be fulfilled for reusing this area. Information about the restrictions and the conditions is available as mostly textual, non-spatial data associated to areas overlapping the target areas. Due to the large amount of possible combinations of restrictions and conditions overlapping (partially) the target area, this decision process becomes quite tedious and cumbersome. Moreover, it proved to be useful to identify similar regions that have reached different stages of development within the data set which in turn allows determining common tasks for these regions. We support the experts in accomplishing these tasks by providing aggregated representations as well as multi-coordinated views together with category filters and selection mechanisms implemented in an interactive decision support system. Textual information is linked to these visualizations enabling the experts to justify their decisions. Evaluating our approach using a standard SUS questionnaire suggests, that especially the experts were very satisfied with the interactive decision support system.
Accepted as Fullpaper by IEEE CG&A.
A chatbot can automatically process a user's request, e.g. to provide a requested information. In doing so, the user starts a conversation with the chatbot and can specify the request by further inquiry. Due to the developments in the field of NLP in recent years, algorithmic text comprehension has been significantly improved. As a result, chatbots are increasingly used by companies and other institutions for various tasks such as order processes or service requests. Knowledge bases are often used to answer users queries, but these are usually curated manually in various text files, prone to errors. Visual methods can help the expert to identify common problems in the knowledge base and can provide an overview of the chatbot system. In this paper, we present Chatbot Explorer, a system to visually assist the expert to understand, explore, and manage a knowledge base of different chatbot systems. For this purpose, we provide a tree-based visualization of the knowledge base as an overview. For a detailed analysis, the expert can use appropriate visualizations to drill down the analysis to the level of individual elements of a specific story to identify problems within the knowledge base. We support the expert with automatic detection of possible problems, which can be visually highlighted. Additionally, the expert can also change the order of the queries to optimize the conversation lengths and it is possible to add new content. To develop our solution, we have conducted an iterative design process with domain experts and performed two user evaluations. The evaluations and the feedback from our domain experts have shown that our solution can significantly improve the maintainability of chatbot knowledge bases.
Accepted as Fullpaper at WSCG 2022.