Question answering systems can process various types of data, including but not limited to:

  1. Textual data: Most question answering systems are designed to handle natural language text data. They can process and analyze sentences or paragraphs of text to understand the context and extract relevant information.

  2. Structured data: Some question answering systems can also work with structured data, such as databases or tables. They can query the structured data to find the appropriate information and provide answers based on that.

  3. Knowledge bases: Question answering systems can also access knowledge bases or curated collections of information, such as Wikipedia or specific domain-specific databases. They can search through this knowledge base to find relevant information and generate answers.

  4. Multimedia data: Advanced question answering systems can handle multimedia data, including images, audio, and videos. They can analyze and interpret visual or auditory information to provide accurate answers.

  5. Ontologies and semantic networks: Question answering systems can utilize ontologies and semantic networks to understand the relationships between different entities and concepts. This helps them in providing more contextual and accurate answers.

  6. Real-time streaming data: Some question answering systems are designed to work with real-time streaming data, such as social media feeds or news articles. They can continuously process and analyze the incoming data to generate timely and up-to-date answers.

  It is important to note that the capabilities of question answering systems may vary depending on their design, architecture, and the specific use case they are built for.