Reinforcement Learning Method for Knowledge Graph Reasoning

Development of reinforcement learning approach for knowledge graph reasoning

Access the dataset

For dawnloading NELL-995 dataset run upload.sh script; FB15k-237 is avaliable here

How to run our code

1. unzip the data, put the data folder in the code directory

2. run the following scripts within scripts/

   • ./pathfinder.sh ${relation_name} # find the reasoning paths, this is RL training, it might take sometime
   • ./fact_prediction_eval.py ${relation_name} # calculate & print the fact prediction results
   • ./link_prediction_eval.sh ${relation_name} # calculate & print the link prediction results

   Examples (the relation_name can be found in NELL-995/tasks/):

   • ./pathfinder.sh concept_athletehomestadium
   • ./fact_prediction_eval.py concept_athletehomestadium
   • ./link_prediction_eval.sh concept_athletehomestadium

3. Reasoning path is arleady put in the dataset, you can directly run fact_prediction_eval.py or link_prediction_eval.sh to get the final results for each reasoning task

Format of the dataset

1. raw.kb: the raw kb data from NELL system
2. kb_env_rl.txt: we add inverse triples of all triples in raw.kb, this file is used as the KG for reasoning
3. entity2vec.bern/relation2vec.bern: transE embeddings to represent out RL states, can be trained using TransX implementations by thunlp
4. tasks/: each task is a particular reasoning relation
   • tasks/${relation}/*.vec: trained TransH Embeddings
   • tasks/${relation}/*.vec_D: trained TransD Embeddings
   • tasks/${relation}/*.bern: trained TransR Embedding trained
   • tasks/${relation}/*.unif: trained TransE Embeddings
   • tasks/${relation}/transX: triples used to train the KB embeddings
   • tasks/${relation}/train.pairs: train triples in the PRA format
   • tasks/${relation}/test.pairs: test triples in the PRA format
   • tasks/${relation}/path_to_use.txt: reasoning paths found the RL agent
   • tasks/${relation}/path_stats.txt: path frequency of randomised BFS