Intelligent Dialogue Management and Knowledge Infusion in Human Computer Interaction (ChatHCI)

Chatbots and Conversation Agents are software applications that mimic human-like discussions with humans by using AI and Natural Language Processing. Several businesses, including e-commerce, healthcare, finance, and education, use Conversation Agents. Websites, chat services, smartphone applications, and even actual physical devices like smart speakers might incorporate them. The importance of Conversation Agents has grown significantly in recent years due to the increasing demand for personalised, efficient, and convenient communication channels with the users. At the same time, Knowledge Graphs are becoming increasingly important in the development of Conversational Agents and Chatbots. A Knowledge Graph is a type of database that represents knowledge as a network of interconnected nodes and edges. In the context of conversational systems, Knowledge Graphs can be used to represent the entities, relationships, and concepts that are relevant to a particular domain. One of the key benefits of using Knowledge Graphs in conversational systems is that they enable more natural and intuitive interactions between users and machines. By modelling the relationships between different pieces of information, Knowledge Graphs can help Conversational Agents better understand the context and intent of a user’s input, and provide more relevant and accurate responses. In addition to improving the accuracy and relevance of conversational systems, Knowledge Graphs also facilitate more efficient and effective dialogue management. Because Knowledge Graphs can represent complex relationships and dependencies between different pieces of information, Conversational Agents can use them to dynamically generate and modify dialogue based on user input, context, and other factors. This can help conversational systems adapt to changing user needs and preferences, and provide a more seamless and personalised experience.

This special session focuses on the role of Knowledge Graphs in addressing key challenges in the development of Chatbots and Conversational Agents, including Natural Language Processing (NLP)/Natural Language Understanding (NLU), Dialogue Management, and Language Generation. Knowledge graphs offer a powerful way to represent and reason about the world, enabling conversational agents to provide intelligent responses to user queries. However, a number of challenges must be addressed in order to achieve truly effective conversational agents, such as Context Understanding (clarifications, temporal aspects, topic switch), Data and Training (open/closed domain, handle questions/requests, argumentation), User Experience (non-active/non-native speaker), Maintenance and Updates (incremental knowledge updates, dynamic ontology extension). This special session brings together researchers and practitioners to share their insights and experiences in addressing these challenges through the use of knowledge graphs, and to discuss future directions for research and development in this area.