/DeepPath

code and docs for my EMNLP paper "DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning"

Primary LanguagePython

Deep Reinforcement Learning for Knowledge Graph Reasoning

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuravy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.

Access the dataset

Download the knowledge graph dataset NELL-995 FB15k-237

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. Since we already put the reasoning paths 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

If you use our code, please cite the paper

@InProceedings{wenhan_emnlp2017,
  author    = {Xiong, Wenhan and Hoang, Thien and Wang, William Yang},
  title     = {DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {ACL}
}

Acknowledgement