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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 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 configs/${dataset}.sh

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

sh configs/


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/, make



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.

Code Structure

The structure of the code is as follows

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

Data Format

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 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.

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


If you use this code, please cite our paper

  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


Meandering In Networks of Entities to Reach Verisimilar Answers





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