## 5 State of the Art in Conversational AI Platforms
### AI chatbots, KGs and advanced AI technology
#### 5.5.1 KG-based AI Chatbots  
There are many proposed approaches to the recent bibliography that introduce KGs to AI chatbots and QA systems. [PERSON] [130] has proposed the SynchroBot, a dialog system that a) has connectivity to robust and flexible KBs and KGs for extracting in-formation, and b) could use NLP tools to interpret user's questions and NLG techniques to provide proper answers. This ability differs from other QA systems which are either focusing in providing a logical conversation ability to the users, without caring for the richness of their knowledge source, or, on the other hand, can only provide accurate answers without any conversation skills. OntBot was an ontology-based chatbot that relied on the approach proposed by [PERSON] [142]. OntBot used Protege [143] to develop an ontology template which gathers knowledge from e-commerce website APIs. Users can place their questions to a dialog manager that uses NLP such as Facebook Wit.Ai. The dialog manager connects to the ontology template with Python language, searches the KBs and KGs and provides the proper answer to the user with the help of NLU techniques from the dialog manager [144]. Disbot is a Portuguese Disaster Support Dynamic Knowledge chatbot that is implemented with Rasa Framework and is able to answer simple questions by exploiting NLP and ML training and also answer more complex questions that require knowledge extraction from a connected knowledge graph infrastructure. This KG infrastructure is designed to be dynamically updated with data from various sources such as social media and webpages [114]. NUBOT is also a KG-based chatbot that is developed with Rasa Framework and explores the implementation of a chatbot that speaks a dialect of the Pakistani Official Language, the Roman Urdu language. The researchers are focusing on the benefits of using KGs as their main knowledge source, on the difficulties in the NLU of the user's intent in Roman Urdu and on how to develop a system that performs better with less computational cost [116]. Another approach [134] is a chatbot developed with Wit.ai platform that is focusing on how to develop a KG-based chatbot that can assist users of an e-commerce website. The proposed architecture of the chatbot is focusing on how to convert data from e-commerce website APIs to XML/JSON format, store them in a Knowledge Base and then load them to a Protege ontology template that can be accessed by the dialogue manager through NLP techniques and is able to retrieve the asked knowledge. PolarisX-bot is a Dialogflow implemented chatbot that relies on KGs. The designers strongly suggest that KGs chatbots systems is the solution for human-like chatbots. Furthermore, they expand their use by introducing the auto-growing KG with the use of Google's BERT model. BERT model is designed to continuously extract knowledge from various sources that feeds and expands the KG of the chatbot. Their main challenge on their future work is the fine tuning of the relationships' extraction [128]. IBM researchers have designed and implemented an ontology-based chatbot with the IBM Watson Assistant. The researchers chose to develop an OWL ontology-based chatbot because their need was to develop a domain specific chatbot that will be able to provide robust and persistent context to its users. Their methodology relies on knowledge extraction techniques and in the assistance of domain experts which refine and train the chatbot system [130]. Finally, KBot is a KG-based chatbot that focuses on the integration of many Linked Data sources. The developers designed the system with Flask python framework and implemented with Chatterbot service. With the integration of NLU techniques, the system sets SPARQL queries to known Knowledge Bases and external web services. The extracted knowledge is gathered in a knowledge graph and with the help of a chatbot service the users receive their answer [145].