---

-- CODE USED FOR JOINTLY EMBEDDING KNOWLEDGE GRAPHS AND LOGICAL RULES--

---

OUTLINE:

1. Introduction
2. Preprocessing
3. Training
4. Testing
5. How to cite
6. Contact

---

---

1. INTRODUCTION:

---

The codes in the folder KALE/ are used for jointly embedding knowledge graphs and logical rules. We peovide how to run the experiments of Link Prediction task in the following.

---

2. PREPROCESSING

---

To run the experiments, you need to preprocess datasets in the folder datasets/ following the steps below: (1) Change data form: call the program ConvertDataForm.java in the folder src/basic/dataProcess to convert the string form of original data into digital form, and get resultant files “train/valid/test.txt” in the folder datasets/ (2) Propositionalize logic rules: call the program GroundAllRules.java in folder src\basic\dataProcess to propositionalize logic rules in the folder datasets/, and get a resultant file “groundings.txt”

---

3. TRAINING

---

To train a model, you need to follow the steps below: (1) Export KALEProgram.java in the folder src/kale/joint as runnable JAR file, for example, termed as KALE.jar (2) Call the program KALE.jar with parameters, for example, as follows:

java -jar KALE.jar -train datasets\wn18\train.txt -valid datasets\wn18\valid.txt -test datasets\wn18\test.txt -rule datasets\wn18\groundings.txt -m 18 -n 40943 -w 0.1 -k 50 -d 0.2 -ge 0.1 -gr 0.1 -# 1000 -skip 50

You can also change the parameters when running RUGE.jar:

• train: the path of training triples
• valid: the path of validate triples
• test: the path of testing triples
• rule: the path of grounded rules
• m: number of relations
• n: number of entities
• k: embedding dimensionality (default 20)
• d: value of the margin (default 0.1)
• ge: initial learning rate of matrix E for AdaGrad(default 0.01)
• gr: initial learning rate of matrix R for AdaGrad(default 0.01)
• #: number of iterations (default 1000)
• skip: number of skipped iterations (default 50)

The program will train a model with the input parameters, and output 5 files:

• result.log: the specification document
• MatrixE.best: learned entity embeddings
• MatrixR.best: learned realtion embeddings

***Please note that in this new implementation version, KALE is optimized by a new mode, using SGD with AdaGrad and gradient normalization.

---

4. TESTING

---

To evaluate on the test datasets, you need call the program Eval_LinkPrediction.java in the folder src/test

You can also change the input parameters when running:

• iEntities: number of entities
• iRelations: number of relations
• iFactors: embedding dimensionality
• fnMatrixE: the file path of entity embeddings
• fnMatrixR: the file path of realtion embeddings
• fnTrainTriples: the file path of training data (digital form)
• fnValidTriples: the file path of validation data (digital form)
• fnTestTriples: the file path of testing data (digital form)

The program will evaluate on testing data, and report the metrics of MRR, MED, and Hits@N (raw and filtered setting).

---

5. HOW TO CITE

---

When using this data, one should cite the original paper:
@inproceedings{guo2016:KALE,
title = {Jointly Embedding Knowledge Graphs and Logical Rules},
author = {Shu Guo and Quan Wang and Lihong Wang and Bin Wang and Li Guo},
booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
year = {2016},
page = {192-202}
}

---

6. CONTACT

---

For all remarks or questions please contact Quan Wang: wangquan (at) iie (dot) ac (dot) cn .