GraphGen enhances complex relationship processing through the following mechanisms:
- Multi-hop sampling technique: The system supports 2-hop neighborhood sampling by default, which can be changed by modifying the
configs/graphgen_config.yamlhit the nail on the headsampling_hopsparameter (up to 5 hops are supported) to capture cross-entity relationship chains. - Knowledge graph guidance: The generated mapping preserves the implicit relationships in the original text, such as the mapping of the
药物-作用机制-靶点蛋白Multi-level associations are automatically converted into multiple rounds of Q&A. - Style Control: Settings
style=detailedWhen it does, the system generates an answer that contains a chain of reasoning, for example:"...首先通过X机制影响Y，继而导致Z变化..." - Practical Examples: For biomedical texts, it is recommended to use 3-hop sampling in conjunction with knowledge graph visualization for validation (the output is located in the
cache/knowledge_graph), while using theece_threshold=0.15Enhanced generation weights for complex concepts.
Empirical measurements show that this method improves the relational complexity of the generated data by a factor of 2.3 (compared to single-hop sampling).
This answer comes from the articleGraphGen: Fine-tuning Language Models Using Knowledge Graphs to Generate Synthetic DataThe
May not be reproduced without permission:AI productivity tools " How to optimize the ability to express complex relationships in generated data?
