In this paper, we present a multiple knowl-edge representation (MKR) framework and discuss its potential for developing big data artificial intel-ligence (AI) techniques with possible broader im-pacts across different AI areas. Typically, canonical knowledge representations and modern representa-tions each emphasize a particular aspect of trans-forming inputs into symbolic encoding or vectors. For example, knowledge graphs focus on depicting semantic connections among concepts, whereas deep neural networks (DNNs) are more of a tool to per-ceive raw signal inputs. MKR is an advanced AI representation framework for more complete intelli-gent functions, such as raw signal perception, feature extraction and vectorization, knowledge symboliza-tion, and logical reasoning. MKR has two benefits:(1) it makes the current AI techniques (dominated by deep learning) more explainable and generaliz-able, and (2) it expands current AI techniques by integrating MKR to facilitate the mutual benefits of the complementary capacity of each representation, e.g., raw signal perception and symbolic encoding. We expect that MKR research and its applications will drive the evolution of AI 2.0 and beyond.