## 6 The Proposed Architecture  
Based on the survey conducted in this line of research and on our previous work [16], this section presents the proposed architectural design of a KG-based, distributed, and collaborative multi-chatbot conversational AI system for museums (Figure 1 depicts an example instantiation of the proposed system architecture). The proposed architecture is promoting the concept of using a network of distributed and collaborative chatbots (multi-chatbot system) within an organization. Each chatbot in the network has its own aim and scope, a specific thematic/domain conversation flow, its own knowledge, and it can also collaborate with other chatbots in the network, share and retrieve knowledge in a distributed manner. Chatbots in such an architecture can provide autonomous conversational services (based on their skill), and at the same time they are able to collaborate to support complex knowledge acquisition and delivery tasks for the museum visitors. Additionally, our approach will adapt the orchestration-by-host-chatbot solution as it was presented in Section 3.6, taking into account that this structural choice is currently considered by platform developers the most suitable technological approach for distributed multi chatbot collaborative solutions. The proposed architecture (Figure 2) relies on five main components:  
1. A chatbot service that provides access to the distributed and collaborative chatbots of any kind and number. The chatbot service must be available through many channels (e.g., Facebook, Viber) and devices (desktop, tablet, smartphone). 2. A knowledge index store mechanism that identifies the user's intent and selects the appropriate chatbot that can provide specific knowledge to specific visitors. 3. An NLP component for transforming user input from natural to formal language
4. A KG component that utilizes Semantic Web technology (RDF, SPARQL query language, and OWL ontologies) for the representation, linking, reasoning, and querying of knowledge. 5. An NLG component for generating well-defined human-like answers. The aim of the proposed architecture is to develop a conversational AI multi-chatbot distributed system that can deploy human-like conversations such as the following example (taken from a scenario in [PERSON] ([PERSON]) museum in Greece):  
* **Visitor:** What is presented in this museum hall? * **Infobot ([PERSON]):** You are at the hall that the life of [PERSON] is presented. Would you like to learn more about his life? * **Visitor:** Yes, that would be great! * **Infobot ([PERSON]):** Sure, your collection guide will be with you soon. You may always return to our discussion and ask me general questions about the museum. * **\"NK Life\" Collection Chatbot ([PERSON]):** Hello, I am the collection guide of [PERSON] life. He was born in Heraklion. His father originated from the village that his museum is built, Myrtia. Would you like to tell you more or you prefer to see his life through an artefact journey in the museum? * **Visitor:** Let's start the journey! * **\"NK Life\" Collection Chatbot ([PERSON]):** I believe that you are now in front of the first artifact showcase, where [PERSON] Early Life Memorabilia are presented. * **Visitor:** Yes, I can see them. They look so interesting. But who are these women on the picture on the left? * **\"NK Life\" Collection Chatbot ([PERSON]):** Oh, what a lovely picture! These are his mother and his beloved sisters [PERSON] and [PERSON]. Their dresses are so beautiful. And look at the left is also his mother's ring! Would like to hear the story of this ring? * **Visitor:** No, I would like to know about his first book! Figure 2: The proposed five-components multi-chatbot collaborative architecture. * **\"NK Life\" Collection Chatbot ([PERSON]):** His first book was a young novel about love, life and death. He was in Athens, studying law and was so in love with a girl. If you want to learn more about this book or others we should ask it from another guide, my colleague [PERSON] (Books Collection Chabot).