Complex Query Answering

Complex query answering (CQA) focuses on developing methods for retrieving information from knowledge graphs and text corpora in response to intricate, multi-hop queries that require sophisticated reasoning beyond simple keyword matching. Current research emphasizes improving the accuracy and efficiency of CQA systems, particularly through the use of neural network architectures like transformers and message-passing networks, often incorporating techniques from both neural and symbolic reasoning. These advancements are crucial for improving information retrieval in diverse applications, ranging from scientific literature search to complex product discovery, and are driving the development of more robust and scalable knowledge-based systems.