unKR 0.6.1

An Open Source Library for uncertain Knowledge Reasoning

unKR: An Open Source Toolkit for Uncertain Knowledge Graph Representation Learning

English | 中文

unKR is an open source toolkit for Uncertain Knowledge Graph Representation Learning(UKRL). It is based on the PyTorch Lightning framework to decouple the workflow of the UKRL models in order to implement multiple Uncertain Knowledge Graph Embedding(UKGE) methods, which in turn assist knowledge graph complementation, inference and other tasks. The tool provides code implementations and results of various existing UKGE models, and provides users with detailed technical documentation.

🔖 Overview

（图片待修改）

unKR toolkit is an efficient implementation for Uncertain Knowledge Graph Representation Learning(URKL) based on the PyTorch Lightning framework. It provides a refinement module process that can implement a variety of Uncertain Knowledge Graph Embedding(UKGE) models, including UKG data preprocessing(Sampler for negative sampling), model implementation base module, and model training, validation, and testing modules. These modules are widely used in different UKGE models, facilitating users to quickly construct their own models.

There are nine different models available, divided according to whether they are small-sample models or not. unKR has validated the tool on three datasets with seven different evaluation metrics, and the details of the models will be discussed in the following sections.

unKR core development team will provide long-term technical support for the toolkit, and developers are welcome to discuss the work and initiate questions using issue.

Detailed documentation of the unKR technology and results is available at 📘.

📝 Models

unKR implements nine UKGE methods that partition the model based on whether it is a small-sample model or not. The available models are as below.

Category	Model
Non-small-sample model	BEURrE， FocusE， GTransE，PASSLEAF，UKGE，UKGsE，UPGAT
Small-sample model	GMUC， GMUCp

Datasets

unKR provides three different sources of UKG datasets including CN15K, NL27K, and PPI5K. The following table respectively shows the source of the datasets and the number of entities, relationships, and triples they contain.

Dataset	Source	Entities	Relations	Triples
CN15K	ConceptNet	15000	36	241158
NL27K	NELL	27221	404	175412
PPI5K	STRING	4999	7	271666

Reproduced Results

unKR uses confidence prediction and link prediction tasks for model evaluation in seven different metrics, MSE, MAE, Hits@k(k=1,3,10), MRR, MR, WMRR, and WMR, with raw and filter settings. In addition, unKR adopts a high-confidence filter(set the filter value to 0.7) method for the evaluation.

Here are the reproduced model results on NL27K dataset using unKR as below. See more results in here.

Raw

Model	Confidence Filter(0.7)	MSE	MAE	Hits@1	Hits@3	Hits@10	MRR	MR	WMRR	WMR
BEUrRE	yes	0.089538999	0.222130999	0.106187999	0.312415004	0.454908997	0.234743997	516.1815186	0.239789993	500.4590149
BEUrRE	no	0.089538999	0.222130999	0.080660999	0.252885997	0.377297997	0.190451995	895.388855	0.215119004	708.1762085
FocusE	yes	290.7572937	16.19610023	0.387077987	0.530840993	0.657818019	0.482501	137.5967255	0.486396998	136.9906921
FocusE	no	290.7572937	16.19610023	0.368319988	0.512754977	0.643151999	0.464713991	176.1968079	0.47627601	158.1079865
GMUC	yes	0.01200	0.08200	0.28100	0.40000	0.54000	0.36800	62.00500	0.36800	61.84900
GMUC	no	0.01300	0.08200	0.28700	0.40900	0.53600	0.37500	71.48400	0.37500	71.44700
GMUC+	yes	0.01500	0.10200	0.29000	0.42000	0.57300	0.43800	45.77400	0.38400	49.80800
GMUC+	no	0.01300	0.08600	0.29900	0.44800	0.58200	0.40100	49.41800	0.40100	49.10700
GTransE	yes	39.83544	5.12528	0.16800	0.28700	0.40700	0.25000	1434.63400	0.25300	1435.39700
GTransE	no	39.83544	5.12528	0.13674	0.24476	0.35250	0.21145	2014.54199	0.23173	1749.63757
PASSLEAF	yes	0.023157001	0.051120002	0.39900	0.53600	0.65700	0.49000	182.90300	0.49600	180.81300
PASSLEAF	no	0.023157001	0.051120002	0.36800	0.50000	0.62100	0.45700	213.23500	0.47700	197.71200
UKGE(PSL)	yes	0.028788	0.059144001	0.38700	0.52400	0.64200	0.47700	207.38100	0.48300	203.61700
UKGE(PSL)	no	0.028788	0.059144001	0.35300	0.48500	0.60000	0.44100	252.57700	0.46200	229.01000
UKGsE	yes	0.12202	0.27065	0.03543	0.06695	0.12376	0.06560	2378.45581	0.06561	2336.46582
UKGsE	no	0.12202	0.27065	0.03000	0.05800	0.10800	0.05700	3022.76900	0.06100	2690.49600
UPGAT	yes	0.02922	0.10107	0.37900	0.52000	0.64500	0.47300	114.65800	0.47700	113.82700
UPGAT	no	0.02922	0.10107	0.33900	0.46700	0.58600	0.42600	166.16900	0.45200	141.35800

Filter

Model	Confidence Filter(0.7)	MSE	MAE	Hits@1	Hits@3	Hits@10	MRR	MR	WMRR	WMR
BEUrRE	yes	0.089538999	0.222130999	0.140640005	0.407182992	0.564805984	0.301261991	453.1804504	0.307830006	438.0662231
BEUrRE	no	0.089538999	0.222130999	0.106242001	0.325922996	0.464942008	0.241718993	831.166748	0.274690986	645.0340576
FocusE	yes	290.7572937	16.19610023	0.710326016	0.849794984	0.930020988	0.790171027	82.4626236	0.793618023	82.71473694
FocusE	no	290.7572937	16.19610023	0.662890017	0.809248984	0.90209502	0.748854995	117.9376526	0.770852983	102.0087128
GMUC	yes	0.01200	0.08200	0.33500	0.46500	0.59200	0.42500	58.31200	0.42600	58.09700
GMUC	no	0.01300	0.08200	0.34400	0.46200	0.59200	0.43000	67.92000	0.43200	67.81300
GMUC+	yes	0.01500	0.10200	0.33800	0.48600	0.63600	0.43800	45.77400	0.43800	45.68200
GMUC+	no	0.01300	0.08600	0.37100	0.50500	0.63800	0.46300	45.87400	0.46500	45.49500
GTransE	yes	39.83544	5.12528	0.22200	0.36600	0.49300	0.31600	1377.56400	0.31900	1378.50500
GTransE	no	39.83544	5.12528	0.17914	0.30818	0.42461	0.26475	1957.77161	0.29136	1692.88000
PASSLEAF(DistMult)	yes	0.023157001	0.051120002	0.63000	0.75400	0.86700	0.70900	137.31200	0.71900	136.42900
PASSLEAF(DistMult)	no	0.023157001	0.051120002	0.55500	0.67700	0.78400	0.63500	162.60200	0.67800	150.42000
UKGE(PSL)	yes	0.028788	0.059144001	0.53500	0.67300	0.82100	0.62900	162.37900	0.63700	159.88900
UKGE(PSL)	no	0.028788	0.059144001	0.47600	0.60400	0.74400	0.56600	202.23200	0.60200	182.20000
UKGsE	yes	0.12202	0.27065	0.03767	0.07310	0.13000	0.06945	2329.50073	0.06938	2288.22217
UKGsE	no	0.12202	0.27065	0.03100	0.06200	0.11300	0.06000	2973.23600	0.06400	2641.84000
UPGAT	yes	0.02922	0.10107	0.61800	0.75100	0.86200	0.70100	69.12000	0.70800	69.36400
UPGAT	no	0.02922	0.10107	0.53000	0.65400	0.76500	0.61100	115.00400	0.65800	93.69200

🛠️ Deployment

Installation

Step1 Create a virtual environment using Anaconda and enter it.

conda create -n unKR python=3.8
conda activate unKR

Step2 Install package.

• Install from source

git clone https://github.com/CodeSlogan/unKR.git
cd unKR
python setup.py install

• Install by pypi

pip install unKR

Step3 Model training.

cd ../
cp demo/UKGEdemo.py ./
python UKGEdemo.py

Parameter Adjustment

In the config file, we provide parameter profiles of the reproduced results, and the following parameters can be adjusted for specific use.

parameters:
  confidence_filter:  #whether to perform high-confidence filtering
    values: [0, 0.7]
  emb_dim:
    values: [128, 256, 512...]
  lr:
    values: [1.0e-03, 3.0e-04, 5.0e-06...]
  num_neg:
    values: [1, 10, 20...]
  train_bs:
    values: [64, 128, 256...]

✉️ Citation

If you find unKR is useful for your research, please consider citing the following paper:

@article{
}

😊 unKR Core Team

Southeast University: Jingting Wang, Tianxing Wu, Shilin Chen, Yunchang Liu, Shutong Zhu, Wei Li, Jingyi Xu, Guilin Qi.

🔎 Reference

• NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs. SIGIR 2022 Demo. https://arxiv.org/pdf/2202.12571.pdf
• NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning. SIGIR 2023 Demo. https://arxiv.org/pdf/2304.14678.pdf
• GitHub：https://github.com/zjukg/NeuralKG