Multiple Knowledge Source

Multiple knowledge source integration focuses on enhancing information retrieval and reasoning by combining data from diverse sources, aiming to overcome limitations of single-source approaches and improve accuracy and robustness. Current research emphasizes developing frameworks that effectively fuse structured and unstructured knowledge, often employing large language models (LLMs) and graph neural networks (GNNs) for knowledge representation and reasoning, along with techniques like prompt learning and beam search for improved retrieval and answer aggregation. This field is significant for advancing question answering, dialogue generation, and other knowledge-intensive tasks across various domains, including healthcare, e-commerce, and scientific discovery, by enabling more comprehensive and reliable information processing.