Complex Query

Complex query answering focuses on efficiently and accurately retrieving information from diverse data sources, including knowledge graphs and relational databases, in response to multifaceted user requests. Current research emphasizes developing novel model architectures, such as graph neural networks and transformer-based encoders, and algorithms like meta-learning and query decomposition to handle the inherent complexities of these queries, particularly in incomplete or distributed datasets. These advancements are crucial for improving the accessibility and usability of large datasets, impacting fields ranging from healthcare search to natural language processing and machine learning workflow optimization.