Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums
- How will our chatbot be able to conduct a human-like conversation?
- Does our chatbot have access to all the necessary knowledge in order to be able to provide the right answer in a comprehensive manner?
- How can this be done more effectively with low computational and/or manual effort?
2. Research Methodology
3. Background Knowledge and Preliminaries
3.1. Machine Learning
3.2. Natural Language Understanding
3.3. Knowledge Graphs
3.4. Semantic Web Technologies
3.5. Human-Like Conversational AI
3.6. Distributed Collaboration in Multi-Chatbot Systems
4. State of the Art in AI Museum Chatbots
4.1. Description of Selective Museum Chatbots
- At the National Art Museum of the Republic of Belarus, a simple QA Facebook Messenger chatbot has been developed with use of the Chatfuel chatbot platform . The chatbot can answer simple questions about the artefacts of the museum and it can be identified as a simple infobot, with low quality conversational skills . The bot was accessible on 30 August 2021 from https://m.me/1904070043163955, but is currently inactive.
- At Anne Frank House in Amsterdam, an off-site Facebook Messenger chatbot was developed and implemented with the use of msg.ai platform, now established as Netomi chatbot platform . The chatbot content is well curated as the user is guided to follow a predetermined route with the use of buttons through the life of Anne Frank and the museum’s exhibits. The chatbot is always tries to be in control of the conversation. It is not designed to answer free questions and gets confused when this happens. The designed scenario follows the scope of the museum, is helpful for the users and safe for the museum as it provides protection from users’ misbehavior [6,9]. The chatbot is currently inactive.
- A chatbot with the same features as the Anne Frank House chatbot is also implemented for the Museo di arte moderna e contemporanea di Trento e Rovereto . The Martmuseum bot uses Facebook Messenger to guide visitors through its content with the use of predefined routes and was active on 30 August 2021 from https://www.messenger.com/t/martrovereto.
- The Maxxi’s Chatbot is designed for the National Museum of the 21st Century Arts in Rome. The chatbot is developed with Google’s Dialogflow and integrated in a Facebook page . The Facebook Messenger chatbot uses predefined content and guides the users through word or image selections to follow a certain path in a guided route. The chatbot is lacking conversational skills but provides engagement through multimedia content, rewards and carefully designed dialogs . The chatbot was accessible with a Facebook account on 30 August 2021 from https://m.me/museomaxxi.
- At the House Museum of Milan an advanced Facebook Messenger chatbot was developed, named “Di Casa in casa” adventure chatbot. The chatbot is developed with the Wit.ai platform and uses gamification techniques in order to engage sole users or groups of visitors to play a “treasure hunt” tour game by finding clues and learning new things. The chatbot also tries to engage visitors by creating realistic non-linear narrative dialogue tours, though the chatbot has no free conversational skills [5,6,9]. The chatbot was active on 30 August 2021 from https://www.messenger.com/t/casemuseobot, and is accessible only in the Italian language.
- At Catal Hoyuk Neolithic site an advanced Facebook Messenger chatbot, named ChatCat was developed by a group of experts. The development team of the ChatCat chatbot, after an extensive design and testing process on the content and the possible dialogues have chosen Wit.ai chatbot platform for the implementation of the chatbot. The reason for their choice was that Wit.ai is focusing on developing chatbots for the popular channel of Facebook Messenger, is supported by an active users’ community and is providing a user-friendly interface for the development of the chatbot. The chatbot had the skill not only to provide useful content but also to provoke the user about a certain issue. The chatbot surprises its users, takes control of the conversation, and awakes emotions and thoughts. It is designed to make rule-based conversations that have a meaning, e.g., the chatbot can start a conversation about the concept of death in the Neolithic period. The final scope of the chatbot is to alter the perception of the users about an issue and engage them . The developers of the ChatCat chatbot are letting the users to use their own words but try to limit their verbal interaction by controlling the conversation. If a user tries to be more creative in his conversation, the chatbot cannot understand and continuously tries to take control of the conversation. The chatbot was accessed with a Facebook Messenger account on 30 August 2021 from https://www.messenger.com/t/catalhoyukbot, but it is currently inactive.
- The chatbot at The Field Museum of Chicago is representing the largest dinosaur relic and is called Máximo the Titanosaur. The chatbot is developed and trained with Google’s Dialogflow platform. One of the main reasons that the developers chose Dialogflow was that the chatbot can be delivered to end-users via many channels (e.g., Facebook, Viber, WeChat, etc.). The developers designed a friendly, smart and delightful conversational chatbot, that is able to answer every possible question in a human-like manner. The developers used the NLU and ML techniques that Dialogflow provides, but the most important work was the continuous user testing and redesign of the possible dialogues. The developers shared the following concept: “For an agent that’s meant to be exploratory and conversational, if it does not sound human, then it’s just not...fun.” . Maximo was online on 30 August 2021 fromhttps://www.fieldmuseum.org/exhibitions/maximo-titanosaur?chat=open, is designed to have a funny character and even to understand your sentiments. The chatbot was launched at May 2019 and by mid-October had 7.000 conversations with 72% accurate response rate .
- The Vincent Van Gogh Museum chatbot was designed to assist museum personnel to provide (via a Facebook page) useful information about the museum and Van Gogh artwork. The chatbot was developed with the open-source version of Microsoft Bot Framework and is entirely hosted on the Museum’s paid Microsoft Azure cloud Server (paid version), providing total control over training, management, and development to the museum . The chatbot is designed with the aim to be conversational, as it allows free questions, however, it cannot successfully complete a human-like conversation. The chatbot identifies the user’s intent and provides text information and web links. The chatbot can be classified as an infobot and was accessible on 30 August 2021 from https://www.facebook.com/messages/t/127104175596.
- Andy Carnegie Bot is a digital character that guides visitors to summer Carnegie Museums of Pittsburgh. Andy also provides updates on events and museum activities. The chatbot was developed with Botpress open-source platform [9,63]. The chatbot is using a gamification process with QR codes and tries to gain visitors engagement through a predefined treasure hunt game that helps visitors collect stamps digitally . The developers of Andy did not focus to engage the visitors through human-like conversations. Instead, they chose to brand the chatbot with interesting graphics, fun visual and text elements, and a fun hunting game. The development team conducted large-scale research to reach to the final decisions for the design and the development of Andy Carnegie Bot . The chatbot was active on Facebook Messenger only on summers (30 August 2021) from https://www.facebook.com/carnegiebot/.
- The Museum of Tomorrow at Rio de Janeiro is a newly established science museum with futuristic architecture. Its main exhibition presents to the public a structured narrative of 5 science areas: Cosmos, Earth, Anthropocene, Tomorrow and Us, where 62 science experiments are presented, curated with high technological applications. One of these applications is IRIS, a chatbot that is installed inside the museum in several screen spots. The visitors receive at their entrance a smart card and anytime they spot an IRIS screen they can talk to it. IRIS is developed with IBM Watson framework and is designed as a chatbot that uses NLU and ML techniques to provoke the users to conduct a dialogue about certain humanity issues and express their concerns. At the end of the dialog IRIS suggests a related local initiative that visitors can participate. The chatbot was trained for many months with the use of testing teams and several testing methods. IRIS speaks Portuguese and English and is mainly a verbal chatbot that interacts via spoken words. However, due to the need of serving hearing-impaired visitors, a text input interface version had been also developed. In addition, IRIS collects all the concerns of the visitors and visualize them in a big screen constellation of colored particles that connect randomly, with a subtle, in a continuous motion. For this functionality the chatbot uses the IBM Bluemix cloud platform, where all the knowledge can be collected and visualized appropriately [9,66,67,68,69]. IRIS chatbot is only accessible to the visitors of the museum and it cannot be evaluated. A video presentation was available on 30 August 2021 from https://www.youtube.com/watch?v=o944e3x-hq4
- The museum of Modern Art at Buenos Aires developed the “Dialogue with the artwork” chatbot. The chatbot is designed with NLU and ML techniques and its innovation is that the visitor could chat with each artwork when she/he was in front of it. For each artwork a different chatbot with its own artistic character was developed. The chatbots are accessible through Facebook Messenger and could conduct smart, funny, and informative human-like dialogues . There is no further information about this chatbot and is currently not active. However, the idea of developing a chatbot for each artwork is rather intriguing and aligned to a distributed and collaborative chatbot architecture proposed in this paper.
- Pinacoteca of São Paulo developed in 2017 the “Voice of Art” chatbot [9,71]. The chatbot is a native mobile voice chatbot developed with IBM Watson and IBM Bluemix cloud platform. The chatbot is using NLU and ML techniques and its training lasted around one year. The users can chat with seven artworks of the museum. The artworks communicate with the mobile app via smartphone beacon sensors and Bluetooth geolocation technology. The knowledge was stored in IBM Bluemix cloud platform and was collected from various sources. The chatbot was able to answer verbally questions about the artwork and do it in human-like manner . The idea of developing a chatbot for each artwork is aligned to a distributed and collaborative chatbot architecture proposed in this paper. “Voice of Art” chatbot is only accessible to the visitors of the museum and it cannot be evaluated. A video presentation was available on 30 August 2021 from: https://www.youtube.com/watch?v=ogpv984_60A&t=185s.
- Ask Mona Studio is a startup chatbot company that had developed around 44 chatbots for museums and cultural organizations in France . There are no clear descriptions in the bibliography of the technologies that Ask Mona Studio is using. It appears that a chatbot platform similar to Chatfuel has been used, a platform that develops simple predefined QA Facebook Messenger chatbots [74,75]. The designed chatbot conversations are predefined and curated with multimedia material. Users must follow a certain path, and every time they try to place a free-text question, the chatbot answers with a predefined answer or a message/reply of ignorance . One of the chatbots, Ask Sarah chatbot at Petit Palais—City of Paris Fine Art Museum was available and active in Facebook Messenger on 30 August 2021 from https://www.messenger.com/t/273861966942/.
- The Culture Chatbot project  is an EU funded project that is initiated by the Jewish Heritage Network  and is implemented in three museums: The Museum of History of Polish Jews (POLIN) in Warsaw , the Jewish Historical Museum in Amsterdam and the Institute for the Union Catalogue of Italian Libraries and Bibliographic . The project has researched the available (at that time) chatbot technologies and reached to three types of that could be implemented in the three partner museums: free-text search chatbot, guided search chatbot and engagement search chatbot. The platform that was chosen for developing the chatbots was Rasa chatbot platform . The chatbots were developed to use NLU and ML techniques and designed to deliver a human-like conversation. In addition, they were designed to be connected to knowledge graphs . The chatbots can be integrated in a webpage or a Facebook page and are bilingual. One of this chatbots was available in the Jewish Historical Museum in Amsterdam on 30 August 2021 from http://chatbot.jck.nl/ and is a guided search chatbot. The chatbot is not fully trained yet and cannot answer free-text questions in a human-like manner. In addition, it is not clear in the bibliography that a KG was developed and implemented.
- The Cartier Foundation for Contemporary Art in France developed a chatbot that is available at https://www.fondationcartier.com/en/ (30 August 2021). The chatbot was developed with Google’s Dialogflow and was designed to work with predefined questions and answers. The chatbot allows users to place their own free-text questions . The chatbot can understand the intent of the questions but its training is limited. Irrelevant questions are answered with a predefined answer or an answer of ignorance and the chatbot tries to control/guide the conversation by providing to the users predefined multimedia content .
- The Akron Art Museum in Cleveland developed a fun Facebook chatbot named Dot [9,85,86]. The Dot is a guide chatbot with a well curated guided tour dialogue. Dot is friendly, fun, and smart but not clever enough, since it cannot answer any free question. However, Dot manages to keep up the interest and the excitement of museum’s visitors with her “character” and the well-curated content and dialog design. Dot was accessible on 30 August 2021 from https://www.messenger.com/t/380485969025647.
- Simple QA informational chatbots (infobots), that can only provide simple information about the museum and its collections, with limited conversational skills. An example of an infobot is the National Art Museum of the Republic of Belarus QA Facebook Messenger chatbot.
- Chatbots with predefined routes of conversation, where the users get information about the museums by following predefined conversation routes with the assistance of text or image buttons. Their conversational skills are limited. The chatbots at Anne Frank House in Amsterdam and the National Museum of the 21st Century Arts in Rome are classified in this type of chatbots.
- Gamification and reward chatbots, where the users also follow predefined routes of conversation but the chatbot developers try to engage them with rewards and gamification features like a treasure hunt. Their conversational skills are limited. Maxxi’s Chatbot and the chatbot at the House Museum of Milan are chatbots that use gamification techniques and rewards to engage users.
- Provoking conversational chatbots, where the chatbots try to control the conversation and provoke the users to ask questions that concern certain information. These chatbots have good conversational skills. Two examples of this type of chatbots are the Catal Hoyuk Neolithic site chatbot and the IRIS chatbot at Museum of Tomorrow at Rio de Janeiro.
- Fully human-like conversational chatbots, where the users can freely ask almost anything, without following any rules or predefined routes. The chatbots have the ability to understand the user’s intent and provide most of the times the right answer. The Máximo the Titanosaur chatbot at Field Museum of Chicago is such a chatbot.
4.2. Evaluation of Museum Chatbots
- Integration of KGs for enhancing the precision and coverage of conversations;
- Distributed knowledge facilitation via multi-chatbots and KGs;
- Multi-chatbots collaboration for knowledge acquisition and delivery.
4.3. The AHP Evaluation Method
- State the problem and identify all the attributes and sub attributes that determine the problem;
- Structure the hierarchy of different attributes;
- Compare each attribute pairwise in its hierarchy level and measure them on a qualitative and quantitative numerical scale (1/9: least valued to 9/9: most important);
- Calculate the weights of each attribute and place them in a comparison attribute matrix;
- Determine the final comparison weight of each attribute;
- Conduct the evaluation.
4.4. Evaluation Process
- Select chatbots that have conversational skills;
- Select chatbots that integrate KGs;
- Evaluate chatbots using the proposed quality evaluation attributes and the AHP evaluation method.
- Máximo the Titanosaur Webpage chatbot;
- IRIS at Museum of Tomorrow at Rio;
- Dialogue with the artwork chatbot (Buenos Aires);
- Voice of Art Chatbot (Pinacoteca of São Paulo);
- EU Culture Chatbot project;
- Cartier Foundation for Contemporary Art chatbot.
5. State of the Art in Conversational AI Platforms
- The conversation-oriented platforms are not designed to serve a specific task but to develop chatbots that follow a specific conversation with humans (or software agents), with no use of advanced AI techniques. An example of the most famous platforms in this category is PandoraBots .
- The conversational AI platforms backed by tech giants are the most advanced platforms that are using advanced AI techniques (NLU and ML) and integrate large knowledge sources such as webpages, ontologies, KGs and others. Examples of the most famous platforms in this category are Google’s Dialogflow , Facebook’s Wit.Ai , ΙΒΜ’s Watson Assistant , Microsoft’s Bot Framework  and Amazon’s Lex .
5.1. Conversational AI Platforms Backed by Startup Companies
5.1.1. Botpress (BP)
5.1.2. OpenDialog (OD)
5.1.3. Rasa Framework (RF)
5.1.4. SnatchBot (SB)
5.1.5. Kore.ai (Kai)
5.2. Conversational AI Platforms Backed by Tech Giants
5.2.1. Dialogflow (DF)
5.2.2. IBM Watson Assistant (IWA)
5.2.3. Facebook Wit.ai (Wit)
5.2.4. Microsoft Bot Framework (MBF)
5.2.5. Amazon Lex (Lex)
5.3. Discussion on the Presented Chatbot Platforms
5.4. Platforms’ Evaluation Using Analytic Hierarchical Process (AHP)
5.4.1. Evaluation Attributes
- Knowledge Graphs (KGs). Use of KGs for representing and integration of specific domain (museum) and general knowledge.
- Natural Language Understanding (NLU). Use of NLU techniques to interpret natural language (that humans understand) to formal language (that machines understand) and the opposite.
- API interaction (API). It is essential for developing a system that does not rely only on trained conversations that are built in the platform, but can connect, search and extract knowledge from external sources and databases.
- Distributed collaboration (DC). All the components of the system must be designed to function autonomously but at the same time to be able to collaborate using a multi-chatbot network. This attribute also allows the smooth transfer of the designed components of a chatbot to any other platform.
- License (LS). Examine if the use of a fully open-source platform is an adequate solution, or a paid version is more flexible for the developers.
- Multilingual support (MS). The platforms must provide multi-lingual support for the encoded conversations, especially in environments where users come from multilingual communities, as tourists visiting a museum. Moreover, the platforms must allow the extension to new languages.
- Multiple channels support (MCS). The chatbots that are developed must be easily deployed via as many channels as possible, such as FB Messenger, WeChat, webpages, Twitter, Slack, Alexa, and others.
- Machine Learning (ML). Use of ML techniques to train chatbots to understand simple or complex questions and provide the right answer with a high precision.
- Additional capabilities (AC). In this attribute we can add the sentiment analysis, the ability of adding personality traits to the chatbot and other special features.
- Text-to-Speech (TTS) and Speech-to-Text (STT). The platform must be able to translate speech to text and the opposite. The ability must be either an in-house ability or to be provided (via APIs) by external applications.
- Self-configuration (SC). This attribute is controversial as self-configuration of chatbots is essential for museums with limited IT support to easily manage the deployed chatbots. On the other hand, this feature does not give the developers much flexibility to use or test other components and integrate them to the chatbot solution.
5.4.2. Evaluation Process
5.4.3. Results and Discussion
5.5. AI chatbots, KGs and advanced AI technology
5.5.1. KG-based AI Chatbots
5.5.2. AI Chatbots Based on Advanced AI Technologies
6. The Proposed Architecture
- 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).
- A knowledge index store mechanism that identifies the user’s intent and selects the appropriate chatbot that can provide specific knowledge to specific visitors.
- An NLP component for transforming user input from natural to formal language
- A KG component that utilizes Semantic Web technology (RDF, SPARQL query language, and OWL ontologies) for the representation, linking, reasoning, and querying of knowledge.
- An NLG component for generating well-defined human-like answers.
- Visitor: What is presented in this museum hall?
- Infobot (Eleni): You are at the hall that the life of Nikos Kazantzakis is presented. Would you like to learn more about his life?
- Visitor: Yes, that would be great!
- Infobot (Eleni): 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 (George): Hello, I am the collection guide of Nikos Kazantzakis 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 (George): I believe that you are now in front of the first artifact showcase, where Kazantzakis’ 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 (George): Oh, what a lovely picture! These are his mother and his beloved sisters Anastasia and Eleni. 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!
- “NK Life” Collection Chatbot (George): 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 John (Books Collection Chabot).
- Visitor: no, it is fine, please tell me the story about when his was a law student.
- “NK Life” Collection Chatbot (George): On the upper right corner of the showcase, you can see a picture of him as a first-year undergraduate at the University of Athens. He studied law….
- Visitor: And where can I buy a book about his life?
- “NK Life” Collection Chatbot (George): For this information you will have to get your answer from my colleague Eleni (InfoBot). Please excuse me. You may always return to our discussion and ask me questions about NK life.
- Infobot (Eleni): Our bookstore is at the entrance of the museum and an online store is always available at our website.
- Museum Chatbot Users (MCU): the visitors of the museum (human agents),
- MuseumBot (MB): the informative chatbot (infobot) software agent
- CollectionBot (CB): the thematic collections chatbot software agents
- Visitors interact with the host chatbot (MB) (via Text, Voice, AR, or VR) to reach the CollectionBot of their interest (CB1 or CB2).
- MB searches the KI-stores using KI-LU, discovers the stored knowledge about the thematic collections and selects which CB can provide the appropriate answer.
- If the KI-store recognizes the intent of a visitors NL-Q, initiates the chosen CB knowledge extraction process. CB, with its NL2FL component, translates the NL-Q of the visitors to formal syntax and sends it to the FL-Q-Exec. At the FL-Q-Exec, FL-Q are turned into SPARQL-q in order to be looked-up to KG-store and retrieve the right answer. KGs are stored at the KG-store (as RDF triples). If an answer is not found in the matched KG-store, an additional search is conducted to the other KGs of the museum or to the connected LOD cloud sources.
- When the matched RDF triples are retrieved, the FL-Q-Exec sends the FL-A to the FL2NL component.
- In the FL2NL component, NLG techniques are used for the transformation of the FL-A to NL-A.
- Finally, visitors receive the NL-As through their device user interface (Text, Voice, AR, VR).
- The integration of KGs contribution in rich structured machine-understandable knowledge to the museum chatbots, solving the problem of limited and unstructured knowledge sources.
- The distributed and collaborative approach contribution in the capability of answering more (if not all) questions, and at the same time delivers a system that can be easily updated and expanded (with new collaborative chatbots).
7. Validating the Approach at Nikos Kazantzakis Museum
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Museum of the Republic of Belarus
|Chatfuel||FB Messenger||No (infobot)|
|Anne Frank House in Amsterdam||Predefined answers |
|Netomi||FB Messenger||Multimedia content|
|Maxxi’s Chatbot||Predefined answers |
|Dialogflow||FB Messenger||Multimedia content and rewards|
|Martmuseum bot||Predefined answers |
|Wit.ai||FB Messenger||Multimedia content|
|House Museum of Milan||Gamification chatbot||Wit.ai||FB Messenger||Gamification, realistic dialogues, |
|Chat Cat bot||Provoking chatbot||Wit.ai||FB Messenger||Provoke the users to follow a path, takes control of the |
|Máximo the Titanosaur Webpage bot||Conversational chatbot||Dialogflow||Webpage||Friendly, smart, loving and delightful chatbot|
|Vincent Van Gogh chatbot app||Predefined answers chatbot||Microsoft Bot Framework||FB Messenger||Multimedia content|
|Andy Carnegie Bot||Gamification chatbot||Botpress||FB Messenger (only|
|Gamification routes with the use of QR codes–Branding the bot with graphics|
|IRIS at Museum of Tomorrow at Rio||Provoking conversational chatbot||IBM Watson||Smart app on screens at the museum||Vocal bot, onsite app, human-like bot|
|Dialogue with the artwork chatbot (Buenos Aires)||Conversational chatbot||-||FB Messenger||Independent chatbots with its own character|
|Voice of Art Chatbot (Pinacoteca of São Paulo)||Conversational chatbot||IBM Watson||native mobile voice app||Vocal bot, onsite app, human-like bot|
|Ask Mona Studio chatbots||Predefined answers |
|FB Messenger||Multimedia content, interesting dialogues, well curated, easily developed|
|EU Culture Chatbot project||Conversational chatbot and predefined answers chatbot||Rasa||FB Messenger and webpage||Goal and knowledge oriented–rich multimedia content|
|Cartier Foundation for Contemporary Art bot||Conversational bot and predefined answers chatbot||Dialogflow||Webpage||Rich multimedia content|
|Akron Art Museum bot||Predefined answers chatbot||-||FB Messenger||Friendly, smart, and delightful chatbot–well curated dialogues and content–has a character|
|Chatbot||Conversational Skills||Advanced AI Methods||KGs Support||Active|
Museum of the Republic of Belarus
|Anne Frank House in Amsterdam||No||No||No||No|
|House Museum of Milan||No||No||No||Yes|
|Chat Cat bot||Rule based conversations||Limited NLU training||No||No|
|Máximo the Titanosaur Webpage bot||Yes||Fully trained, NLU and ML||No||Yes|
|Vincent Van Gogh chatbot app||Yes, not fully developed||Limited NLU training||No||Yes|
|Andy Carnegie Bot||Available but not used||Available but not used||No||Yes|
|IRIS at Museum of Tomorrow at Rio||Yes||NLU and ML||Yes, on IBM Bluemix cloud platform–Graph visualization||Yes|
|Dialogue with the artwork chatbot (Buenos Aires)||Yes||NLU and ML||No||No|
|Voice of Art Chatbot (Pinacoteca of São Paulo)||Yes||NLU and ML||Yes, on IBM Bluemix cloud platform||Not defined|
|Ask Mona Studio chatbots||No (Only predefined answers)||No||No||Yes|
|EU Culture Chatbot project||Available but not used||Available but not used||Yes–Dgraph–not fully developed||Yes, a|
|Cartier Foundation for Contemporary Art bot||Yes, but limited||Available but not used||No||Yes|
|Akron Art Museum bot||No||No||No||Yes|
|1||Both attributes are equal|
|7||Very strongly preferred|
|1||Support for this attribute is unavailable or this attribute does not apply|
|2||Some or very limited support for this attribute is available thanks to, e.g., workarounds or other third-party software.|
|3||The attribute is available, but polishing is required. Has certain limitations that affect its functionality or usability.|
|4||The attribute is available in the system, lacks some minor details such as complete documentation or advanced functionality.|
|5||Support for this attribute in the system is fully present and has no limitations.|
|Weight||Conversational AI Platforms|
Backed By Startup Companies
|Conversational AI Platforms Backed by Tech Giants|
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Varitimiadis, S.; Kotis, K.; Pittou, D.; Konstantakis, G. Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums. Appl. Sci. 2021, 11, 9160. https://doi.org/10.3390/app11199160
Varitimiadis S, Kotis K, Pittou D, Konstantakis G. Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums. Applied Sciences. 2021; 11(19):9160. https://doi.org/10.3390/app11199160Chicago/Turabian Style
Varitimiadis, Savvas, Konstantinos Kotis, Dimitra Pittou, and Georgios Konstantakis. 2021. "Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums" Applied Sciences 11, no. 19: 9160. https://doi.org/10.3390/app11199160