SETUP.md

Setup

This setup documentation serves as an introduction for extending and maintaining the ChronoKGE framework.

Project structure

Folder structure

The chrono_kge module consists of the following folders:

• main: The main modules.
  • handler: Handler for framework state.
  • manager: Managers for framework execution.
  • parser: Parsers for framework parameter.
• experiment: The experiment modules.
• knowledge: Modules related to data handling and preprocessing.
  • augmentation: Augmentation modules.
  • chrono: Time processing modules.
  • graph: Knowledge graph modules.
• model: The model modules.
  • kge: Complete network models.
    • deepml: KGE models using deep learning.
    • geometric: KGE-models using distance scoring.
    • semantic: KGE-models using similarity scoring.
  • module: Model modules and extensions.
    • calculus: Basic tensor modules.
    • embedding: Embedding modules.
    • pooling: Bilinear pooling modules.
    • regularizer: Regularization modules.
    • scoring: Scoring modules.
• statistics: The statistics modules.
• trainer: The trainer modules.
• tuner: The tuner modules.
• utils: Additional helper functions
  • vars: Constants and enumerations.
  • web: Web utilities.
• visualization: Visualization tools.

KGE Families

• Deep Learning (network): model.kge.deepml
• Geometric (distance): model.kge.geometric
• Semantic matching (similarity): model.kge.semantic

Extending ChronoKGE

Adding a new model

In the following, we describe how to add a new embedding model.

1. Add a new model into model/kge/<family>(Learn more) by either:
   1. creating a new model which inherits PyTorch's base nn.Module
   2. extending an existing model, e.g., inheriting from kge_model.KGE_Model
2. Register your model within the model/__model__.py module
   1. Add an entry into the REGISTER dictionary, where:
      1. key: a unique string identifier used to call your model
      2. value: the class type of your model
3. Done!

Adding a new dataset

In the following, we describe how to add a new benchmark dataset.

1. Save the new dataset under one of the following folders:
   1. static: Datasets with static facts (s,p,o)
   2. temporal: Datasets with temporal facts (s,p,o,t)
   3. synthetic: Datasets which are synthetically created
2. Register a new dataset in the knowledge/knowledge_base.py module
   1. Define a new KnowledgeBase in the KnowledgeBases object
      1. Specify the KB properties according to the dataset:
         1. name: a unique string identifier (same name as data-folder)
         2. genre: the genre of the KB (static, temporal, synthetic)
         3. start_date: (optional) The start date of the KB
         4. num_days: (optional) The number of days covered by the KB
         5. gran: (optional) The time granularity of the KB
   2. Add the new KB enum into the ALL_KB list in the KnowledgeBases object
   3. Register the new KB in the appropriate function attributes:
      1. has_timestamps: KB contains time in form of timestamps
      2. has_indices: KB contains entity indices in datasets
      3. has_labels: KB contains entity labels in datasets
3. Done!