Meandering In Networks of Entities to Reach Verisimilar Answers

Code and models for the paper Go for a Walk and Arrive at the Answer - Reasoning over Paths in Knowledge Bases using Reinforcement Learning

MINERVA is a RL agent which answers queries in a knowledge graph of entities and relations. Starting from an entity node, MINERVA learns to navigate the graph conditioned on the input query till it reaches the answer entity. For example, give the query, (Colin Kaepernick, PLAYERHOMESTADIUM, ?), MINERVA takes the path in the knowledge graph below as highlighted. Note: Only the solid edges are observed in the graph, the dashed edges are unobsrved. gif courtesy of Bhuvi Gupta

To install the various python dependencies (including tensorflow)

pip install -r requirements.txt

Training MINERVA is easy!. The hyperparam configs for each experiments are in the configs directory. To start a particular experiment, just do

sh run.sh configs/${dataset}.sh

where the ${dataset}.sh is the name of the config file. For example,

sh run.sh configs/countries_s3.sh

We are also releasing pre-trained models so that you can directly use MINERVA for query answering. They are located in the saved_models directory. To load the model, set the load_model to 1 in the config file (default value 0) and model_load_dir to point to the saved_model. For example in configs/countries_s2.sh, make

load_model=1
model_load_dir="saved_models/countries_s2/model.ckpt"

The code outputs the evaluation of MINERVA on the datasets provided. The metrics used for evaluation are Hits@{1,3,5,10,20} and MRR (which in the case of Countries is AUC-PR). Along with this, the code also outputs the answers MINERVA reached in a file.

The structure of the code is as follows

Code
├── Model
│    ├── Trainer
│    ├── Agent
│    ├── Environment
│    └── Baseline
├── Data
│    ├── Grapher
│    ├── Batcher
│    └── Data Preprocessing scripts
│            ├── create_vocab
│            ├── create_graph
│            ├── Trainer
│            └── Baseline

To run MINERVA on a custom graph based dataset, you would need the graph and the queries as triples in the form of (e₁,r, e₂). Where e₁, and e₂ are nodes connected by the edge r. The vocab can of the dataset can be created using the create_vocab.py file found in data/data preprocessing scripts. The vocab needs to be stores in the json format {'entity/relation': ID}. The following shows the directory structure of the Kinship dataset.

kinship
    ├── graph.txt
    ├── train.txt
    ├── dev.txt
    ├── test.txt
    └── Vocab
            ├── entity_vocab.json
            └── relation_vocab.json

If you use this code, please cite our paper

@inproceedings{minerva,
  title = {Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning},
  author = {Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and Vilnis, Luke and Durugkar, Ishan and Krishnamurthy, Akshay and Smola, Alex and McCallum, Andrew},
  booktitle = {ICLR},
  year = 2018
}