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OBJECTIVE: Disease knowledge graphs have emerged as a powerful tool for AI, enabling the connection, organization, and access to diverse information about diseases. However, the relations between disease concepts are often distributed across multiple data formats, including plain language and incomplete disease knowledge graphs. As a result, extracting disease relations from multimodal data sources is crucial for constructing accurate and comprehensive disease knowledge graphs.METHODS: We introduce REMAP, a multimodal approach for disease relation extraction. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, aligning the multimodal embeddings for optimal disease relation extraction. Additionally, REMAP utilizes a decoupled model structure to enable inference in single-modal data, which can be applied under missing modality scenarios.RESULTS: We apply the REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves language-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with language information. Furthermore, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score).CONCLUSION: In summary, REMAP is a flexible multimodal approach for extracting disease relations by fusing structured knowledge and language information. This approach provides a powerful model to easily find, access, and evaluate relations between disease concepts.