The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base\r\nfor a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations.\r\nThe modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According\r\nto this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into\r\naccount particular features of the dialogue or the user behavior.We illustrate the implementation of a proof-of-concept prototype:\r\na set of modules exploiting different knowledge representation methodologies and capable of managing different conversation\r\nfeatures has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects\r\nin real time the most adequate chatbot knowledge module to activate.
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