Welcome to our guide on how to effectively utilize MINERVA, a powerful Reinforcement Learning (RL) agent designed to answer queries within a knowledge graph of entities and relationships. Through understanding the underlying mechanics of MINERVA, you can enhance your capability to reason over complex datasets. Let’s dive in!

Understanding MINERVA’s Functionality

Think of MINERVA as a skilled tour guide in a vast city (the knowledge graph). Each stop (node) represents an entity, while the city’s streets (edges) connect these entities. To get to the answer entity, MINERVA learns the best paths depending on the query it receives, much like a guide who becomes familiar with the most efficient routes over time.

Requirements

Before you start your journey with MINERVA, ensure you have the necessary tools. Install the required Python dependencies to set everything up:

pip install -r requirements.txt

Training MINERVA

Training your MINERVA agent is a straightforward process. The hyperparameter configurations for various experiments can be found in the configs directory. To initiate a specific experiment, use the following command:

sh run.sh configs/$dataset.sh

For example, to start the training for country data, you would run:

sh run.sh configs/countries_s3.sh

Testing MINERVA

MINERVA comes with pre-trained models ready for immediate use. You can find these models in the saved_models directory. To load a pre-trained model, modify your config file by setting load_model to 1 and specify the model_load_dir to point to the loaded model. For instance:

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

Output and Evaluation Metrics

After training and testing, MINERVA will provide an evaluation of its performance on the datasets, utilizing metrics such as Hits@1, 3, 5, 10, 20, and MRR (Area Under Curve of Precision-Recall) for countries. Additionally, the solutions MINERVA identifies will be saved in a file for your reference.

Code Structure Overview

The internal organization of the MINERVA codebase is as follows:

• Code
  • Model
  • Trainer
  • Agent
  • Environment
  • Baseline
  • Data
  • Grapher
  • Batcher
• Data Preprocessing scripts
  • create_vocab
  • create_graph

Data Format

To implement MINERVA on a custom dataset, your graph and queries should be structured as triples of the format: (esub1, r, esub2), where esub1 and esub2 are the connected nodes, and r is the relationship. The vocabulary for your dataset can be generated using the create_vocab.py script located in the data/data/preprocessing/scripts directory. Ensure the vocabulary is stored in JSON format.

Troubleshooting Common Issues

As you embark on your MINERVA journey, you may encounter some roadblocks. Here are a few troubleshooting tips:

• If you face issues with model loading, verify that the model_load_dir path is correct.
• Check that all required dependencies are properly installed.
• Make sure your query triples are in the correct format for efficient processing.

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Conclusion

With MINERVA’s capabilities, you’re now equipped to traverse knowledge graphs and glean valuable answers from complex data sets. Implementing an RL agent such as MINERVA can be a transformative step in enhancing intelligent systems for data comprehension and retrieval.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.