# KR-EAR

**Repository Path**: thunlp/KR-EAR

## Basic Information

- **Project Name**: KR-EAR
- **Description**: Knowledge Representation Learning with Entities, Attributes and Relations
- **Primary Language**: C++
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-05-30
- **Last Updated**: 2022-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# KR-EAR
Code of IJCAI2016: "Knowledge Representation Learning with Entities, Attributes and Relations".

Evaluation Results
==========

Evaluation results on entity prediction.

| Model      |     MeanRank(Raw) |   MeanRank(Filter)   |	Hit@10(Raw)	| Hit@10(Filter)|
| :-------- | --------:| :------: | :------: |:------: |
| TransE 			| 259		| 200		| 35.8	| 53.0 |
| TransH			| 282		| 224		| 33.9	| 50.2 |
| TransR 			| 260		| 200		| 37.0	| 56.1 |
| KR-EAR(TransE)	| 186		| 133		| 38.5	| 54.5 |	
| KR-EAR(TransR)	| 172		| 118		| 39.5	| 57.3 |

Evaluation results on relation prediction.

| Model      |     MeanRank(Raw) |   MeanRank(Filter)   |	Hit@10(Raw)	| Hit@10(Filter)|
| :-------- | --------:| :------: | :------: |:------: | 
| TransE			| 3.1	| 2.8	| 65.9	| 83.8 |
| TransH			| 3.4	| 3.1	| 64.9	| 84.1 |
| TransR  			| 3.4	| 3.1	| 65.2	| 84.5 |
|  KR-EAR(TransE)	| 2.4	| 2.1	| 67.9	| 86.2 |
|  + CRA			| 1.8	| 1.6 | 70.9	| 88.7 |
| KR-EAR(TransR)	| 2.6	| 2.2	| 66.8	| 89.0 |
|  + CRA			| 1.9	|1.6	| 71.5	| 90.4 |

Evaluation results on attribute prediction.

| Model      |     MeanRank(Raw) |   MeanRank(Filter)   |	Hit@10(Raw)	| Hit@10(Filter)|
| :-------- | --------:| :------: | :------: |:------: | 
| TransE			| 10.7	| 5.6	| 36.5	| 55.9 |
| TransH			| 10.7	| 5.6	| 38.5	| 57.9 |
| TransR  			| 9.0		| 3.9	| 42.7	| 65.6 |
| KR-EAR(TransE)	| 8.3		| 3.2	| 47.2	| 69.0 |
|  +AC			| 7.5		| 3.0 | 49.4 	| 70.4 |
| KR-EAR(TransR)	| 8.3		| 3.2 	| 47.6	| 69.8 |

DATA
==========

We provide FB24k dataset used for the task knowledge base completion in data.zip, using the input format required by our codes. 


Datasets are required in the folder data/ in the following format, containing nights files:



+ train-rel.txt: training file of relations, format (e1, e2, rel).

+ test-rel.txt: test file of relations, same format as train-rel.txt.

+ train-attr.txt: training file of attributes, format (e1, val, attar).

+ test-attr.txt: test file of attributes, same format as train-attr.txt.

+ entity2id.txt: all entities and corresponding ids, one per line.

+ relation2id.txt: all relations and corresponding ids, one per line.

+ attribute2id.txt: all attributes and corresponding ids, one per line.

+ val2id.txt: : all values and corresponding ids, one per line.

+ attribute_val.txt: the value set of each attribute

Code
==========

The codes are in the folder KR-EAR(TransE)/, KR-EAR(TransR)/.


COMPILE
==========

Just type make in the folder ./

RUN
==========

You need to  type the following command in each model folder:

For training:

	./main

For testing:

	./test

	./test_attr

You can also change the parameters when training.

-n : the embedding size of entities, relations

-m :  the embedding size of values 

-margin: the margin length








CITE
==========


If you use the code, please kindly cite the following paper:

Yankai Lin, Zhiyuan Liu, Maosong Sun. Knowledge Representation Learning with Entities, Attributes and Relations. International Joint Conference on Artificial Intelligence (IJCAI 2016).