graph LR
Graph2Seq["Graph2Seq"]
Graph2Tree["Graph2Tree"]
Complex["Complex"]
Distmult["Distmult"]
Base_Models["Base Models"]
GNN_Modules["GNN Modules"]
StdRNNDecoder["StdRNNDecoder"]
TreeBasedDecoder["TreeBasedDecoder"]
ComplExLayer["ComplExLayer"]
DistMultLayer["DistMultLayer"]
Graph2Seq -- "uses" --> GNN_Modules
Graph2Seq -- "uses" --> StdRNNDecoder
Graph2Seq -- "inherits from" --> Base_Models
Graph2Tree -- "uses" --> GNN_Modules
Graph2Tree -- "uses" --> TreeBasedDecoder
Graph2Tree -- "inherits from" --> Base_Models
Complex -- "delegates to" --> ComplExLayer
Distmult -- "delegates to" --> DistMultLayer
The graph4nlp project's core architecture for graph-to-sequence and graph-to-tree tasks revolves around two primary orchestrators: Graph2Seq and Graph2Tree. Both inherit from Base Models, establishing a common foundation for graph-based neural network models. Graph2Seq leverages GNN Modules for graph representation learning and StdRNNDecoder for sequence generation, making it suitable for tasks like neural machine translation. Similarly, Graph2Tree utilizes GNN Modules for graph encoding but employs a TreeBasedDecoder to produce structured tree outputs, catering to tasks suchs as semantic parsing. For knowledge graph completion, the Complex and Distmult models are provided, which delegate their core prediction logic to ComplExLayer and DistMultLayer respectively. This modular design allows for flexible integration of various GNN encoders and decoders, promoting reusability and extensibility across different graph-based NLP tasks.
Orchestrates the end-to-end Graph-to-Sequence pipeline, managing the flow from input graph processing through graph representation learning to output sequence decoding. It is designed for tasks like Neural Machine Translation or Summarization.
Related Classes/Methods:
Orchestrates the end-to-end Graph-to-Tree pipeline, integrating graph representation learning with the generation of tree structures. This is crucial for tasks such as semantic parsing where the output is a structured tree.
Related Classes/Methods:
Implements the ComplEx model, a specific knowledge graph embedding model used for Knowledge Graph Completion tasks. It handles the complex-valued embeddings and their interaction for link prediction.
Related Classes/Methods:
Implements the DistMult model, another knowledge graph embedding model for Knowledge Graph Completion. It focuses on a simpler, element-wise multiplication interaction for link prediction.
Related Classes/Methods:
Provides a foundational abstract layer for building various graph neural network (GNN) encoders and embedding encoders. It promotes code reuse and defines common interfaces for higher-level models.
Related Classes/Methods:
Represents various Graph Neural Network (GNN) layers used for learning graph representations.
Related Classes/Methods:
A standard RNN-based decoder for generating output sequences.
Related Classes/Methods:
A specialized tree-based decoder for generating structured tree outputs.
Related Classes/Methods:
Component responsible for the core complex-valued prediction logic in the ComplEx model.
Related Classes/Methods:
Component responsible for the core element-wise multiplication prediction logic in the DistMult model.
Related Classes/Methods: