The difference between open-domain and closed-domain question answering lies in the scope of the questions that can be answered.

  Open-domain question answering deals with a wide range of topics and attempts to answer questions by accessing vast amounts of information from various sources, such as articles, books, websites, and databases. Open-domain systems aim to provide comprehensive and general knowledge on a broad range of subjects.

  In contrast, closed-domain question answering focuses on specific domains or topics and provides answers based on a specific set of pre-defined knowledge. These systems are designed to address questions within a limited scope or domain, such as a specific website, a specific database, or a particular subject area like medicine or law.

  The approach used in open-domain question answering often involves using natural language processing techniques to understand the question, identify relevant information, and extract the answer from large amounts of unstructured data. These systems may employ various strategies, including information retrieval, document ranking, and semantic analysis to generate an appropriate response.

  On the other hand, closed-domain question answering typically relies on pre-existing knowledge bases or structured databases. These systems are tailored to a specific domain and leverage the structured information within that domain to provide accurate and precise answers. Closed-domain question answering systems may employ rule-based approaches, knowledge graphs, or ontology to match the question with the available knowledge and retrieve the most relevant response.

  In summary, open-domain question answering aims to answer questions across a wide range of topics, leveraging vast amounts of unstructured data, while closed-domain question answering focuses on specific domains or topics using pre-defined knowledge to provide accurate answers within a limited scope.