edwinxuh / kr-ear

Knowledge Representation Learning with Entities, Attributes and Relations

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).