Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

Abstract

Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.

Chatbots in Museums: Is Visitor Experience Measured?

“…Application 11: Collaborative Chatbots (Varitimiadis et al 2021) Objective: The team that created the MuBot (Varitimiadis et al 2020) application has also published more recent work in which the importance of knowledge graphs is emphasized and a distributed architecture of multiple chatbots (collaborative chatbots) is outlined, an important trend towards which research in machine learning and language processing is moving. A knowledge graph describes real-world entities and their relationships in the form of an oriented graph.…”

Cultural tourist and user experience with artificial intelligence: a holistic perspective from the Industry 5.0 approach

“…However, most of these chatbots do not provide a human-like conversation. They do not provide the complete knowledge requested by visitors (Varitimiadis et al, 2021) during all phases of the visit. Varitimiadis et al (2021) classifies chatbots into: informative chatbots (infobots), chatbots with predefined conversation paths, gamification and reward chatbots, conversational chatbots and (5) advanced conversational chatbots, where users can freely ask almost anything without following any rules or routes predefined.…”

“…More personalisation and customisation are required (Pillai and Sivathanu, 2020). Some museums in different countries are developing chatbots to assist their users and provide a different and enhanced user experience (Varitimiadis et al, 2021). Participants consider that the website is the first contact with the cultural institution, being in the future an opportunity to create personalised visits, by implementing, for example, chatbots.…”

Програмне Забезпечення Для Віртуальних Турів

“…More about the capabilities and development of dialog agents in the articles [13,14]. The authors analyze such architectural approaches to the construction of museum dialogue agents as machine learning, connection to knowledge graphs, understanding of natural language.…”

“…The Wit.ai, Google's Dialogflow, IBM's Watson, and other frameworks allow you to develop similar software, but are not at all designed to create a complex dialog system that is a virtual guide. Controlled or uncontrolled algorithms are used in machine-learning chatbots, segmentation and morphological analysis in natural language comprehension methods [13]. The development of software for virtual tours requires special information support, architecture using knowledge graphs and a combination of algorithms for machine learning, processing, understanding and generation of natural language.…”