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ConceptNet — pykeen 1.11.2-dev documentation

Getting Started

• Installation
• First Steps
• Knowledge Graph Embedding Models
• Representations
• Interaction Functions
• Tracking Results during Training
• Saving Checkpoints during Training
• A Toy Example with Translational Distance Models
• Understanding the Evaluation
• Optimizing a Model’s Hyper-parameters
• Running an Ablation Study
• Performance Tricks
• Getting Started with NodePiece
• Inductive Link Prediction
• Splitting
• PyTorch Lightning Integration
• Using Resolvers
• Normalizer, Constrainer & Regularizer
• Troubleshooting

Bring Your Own

• Bring Your Own Data
• Bring Your Own Interaction

Extending PyKEEN

• Extending the Datasets
• Extending the Models

Reference

• Pipeline
• Models
• Datasets
  • pykeen.datasets Package
    • Functions
    • Classes
      • Dataset
      • AristoV4
      • Hetionet
      • Kinships
      • Nations
      • OpenBioLink
      • OpenBioLinkLQ
      • CoDExSmall
      • CoDExMedium
      • CoDExLarge
      • CN3l
      • OGBBioKG
      • OGBWikiKG2
      • UMLS
      • FB15k
      • FB15k237
      • WK3l15k
      • WK3l120k
      • WN18
      • WN18RR
      • YAGO310
      • DRKG
      • BioKG
      • ConceptNet
      • CKG
      • CSKG
      • DBpedia50
      • DB100K
      • OpenEA
      • Countries
      • WD50KT
      • Wikidata5M
      • PharmKG8k
      • PharmKG
      • PrimeKG
      • Globi
      • PharMeBINet
    • Variables
    • Class Inheritance Diagram
  • pykeen.datasets.base Module
  • pykeen.datasets.analysis Module
• Inductive Datasets
• Entity Alignment
• Triples
• Triples Workflows
• Training
• Stoppers
• Loss Functions
• Loss Weighting
• Regularizers
• Result Trackers
• Negative Sampling
• Filtering
• Optimizers
• Evaluation
• Metrics
• Hyper-parameter Optimization
• Ablation
• Prediction
• Uncertainty
• Sealant
• Constants
• Flexible Weight Checkpoints
• pykeen.nn
• Utilities

Appendix

• Analysis
• References

• Datasets
• ConceptNet
• View page source

class ConceptNet(random_state: None | int | Generator = 0, **kwargs)[source]

Bases: SingleTabbedDataset

The ConceptNet dataset from [speer2017].

The dataset is structured into 5 columns (see https://github.com/commonsense/conceptnet5/wiki/Downloads#assertions): edge URL, relation, head, tail, metadata.

Initialize the ConceptNet dataset from [speer2017].

Parameters:

• random_state (None | int | Generator) – The random seed to use in splitting the dataset. Defaults to 0.

• kwargs – keyword arguments passed to pykeen.datasets.base.SingleTabbedDataset.

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