mlsanityData to 14 Oct 2026

BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning

arXiv 2507.14468 ↗First posted 19 Jul 2025cs.AIcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Motivation: Biomedical knowledge graphs (KGs) are crucial for drug discovery and disease understanding, yet their completion and reasoning are challenging. Knowledge Embedding (KE) methods capture global semantics but struggle with dynamic structural integration, while Graph Neural Networks (GNNs) excel locally but often lack semantic understanding. Even ensemble approaches, including those leveraging language models, often fail to achieve a deep, adaptive, and synergistic co-evolution between semantic comprehension and structural learning. Addressing this critical gap in fostering continuous, reciprocal refinement between these two aspects in complex biomedical KGs is paramount. Results: We introduce BioGraphFusion, a novel framework for deeply synergistic semantic and structural learning. BioGraphFusion establishes a global semantic foundation via tensor decomposition, guiding an LSTM-driven mechanism to dynamically refine relation embeddings during graph propagation. This fosters adaptive interplay between semantic understanding and structural learning, further enhanced by query-guided subgraph construction and a hybrid scoring mechanism. Experiments across three key biomedical tasks demonstrate BioGraphFusion's superior performance over state-of-the-art KE, GNN, and ensemble models. A case study on Cutaneous Malignant Melanoma 1 (CMM1) highlights its ability to unveil biologically meaningful pathways. Availability and Implementation: Source code and all training data are freely available for download at https://github.com/Y-TARL/BioGraphFusion. Supplementary information: Supplementary data are available at Bioinformatics online.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 18 of its 35 references that are in the map. First posted on arXiv 19 Jul 2025.

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18 of 35 references.

References · 18

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  2. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  4. 2016Complex Embeddings for Simple Link Predictioncited by 902
  5. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  6. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 396
  7. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 324
  8. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 265
  9. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  10. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  11. 2021Knowledge Graph Reasoning with Relational Digraphcited by 92
  12. 2023Knowledge Graphs: Opportunities and Challengescited by 70
6 more
  1. 2019Probabilistic Logic Neural Networks for Reasoningcited by 69
  2. 2022AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoningcited by 28
  3. 2019Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoningcited by 23
  4. 2022Joint Language Semantic and Structure Embedding for Knowledge Graph Completioncited by 15
  5. 2023Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learningcited by 9
  6. 2019Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embeddingcited by 1

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  1. 2025DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
  2. 2025K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
  3. 2026MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring
  4. 2026Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG
  5. 2026PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
  6. 2026CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
  7. 2023BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights
  8. 2026RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine