https://github.com/yijia-xiao/know2bio
Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs
https://github.com/yijia-xiao/know2bio
benchmark biomedical knowledge-graph
Last synced: 6 months ago
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Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs
- Host: GitHub
- URL: https://github.com/yijia-xiao/know2bio
- Owner: Yijia-Xiao
- License: cc0-1.0
- Created: 2023-06-14T01:48:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-26T04:40:13.000Z (over 1 year ago)
- Last Synced: 2025-04-11T00:17:51.072Z (6 months ago)
- Topics: benchmark, biomedical, knowledge-graph
- Language: Python
- Homepage:
- Size: 74.1 MB
- Stars: 13
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
------------------------------------------------
# Know2BIO
Know2BIO is a comprehensive biomedical knowledge graph harmonizing heterogeneous database sources.
## Getting Started
### Environment Setup
We recommend using Anaconda3 to manage the environment.
- Install Anaconda3.
- Edit `env.yaml`: set $USER_PATH to user's directory.
- Create `know2bio` environment using `conda env create -f env.yml`.
### Hardware Requirements
- Server: AMD EPYC 7542 Processor (128 cores), 1.73 TB RAM, and 8 NVIDIA A100-SXM4-80GB GPUs.
- Operating system: Ubuntu 20.04 LTS.
## Benchmarking
### Setup
- Python environment: follow the guide in `Environment Setup` Section.
### Experiments
- To run the experiments, please execute `main.py` script. Arguments are listed below.
```
usage: main.py [-h] [--dataset {ontology,instance,whole,FB15K,WN,WN18RR,FB237,YAGO3-10}]
[--model {TransE,TransR,DistMult,CP,MurE,RotE,RefE,AttE,RotH,RefH,AttH,ComplEx,RotatE}] [--regularizer {N3,F2}] [--reg REG]
[--optimizer {Adagrad,Adam,SparseAdam}] [--max_epochs MAX_EPOCHS] [--patience PATIENCE] [--valid VALID] [--rank RANK] [--batch_size BATCH_SIZE] [--neg_sample_size NEG_SAMPLE_SIZE]
[--init_size INIT_SIZE] [--learning_rate LEARNING_RATE]
Knowledge Graph Embedding
options:
-h, --help show this help message and exit
--dataset {ontology,instance,whole}
Knowledge Graph dataset: ontology, instance, whole views
--model {TransE,TransR,DistMult,CP,MurE,RotE,RefE,AttE,RotH,RefH,AttH,ComplEx,RotatE}
Knowledge Graph embedding model
--optimizer {Adagrad,Adam,SparseAdam}
Optimizer
--max_epochs MAX_EPOCHS
Maximum number of epochs to train for
--patience PATIENCE Number of epochs before early stopping
--valid VALID Number of epochs before validation
--rank RANK Embedding dimension
--batch_size BATCH_SIZE
Batch size
--neg_sample_size NEG_SAMPLE_SIZE
Negative sample size, -1 to not use negative sampling
--dropout DROPOUT Dropout rate
--init_size INIT_SIZE
Initial embeddings' scale
--learning_rate LEARNING_RATE
Learning rate
```
- Example: Train TransE model on Know2BIO's whole view
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model TransE --dataset whole --valid 10 --patience 5 --rank 512 --neg_sample_size 150 --optimizer Adam --learning_rate 0.001
```
Code and README for the benchmarking Know2BIO can be found in [**benchmark**](./benchmark).
## Dataset Construction
### Dataset Schema
### Data Source and Relationships
### Usage and Datasheet
Code and README for the construction of Know2BIO can be found in [**dataset**](./dataset).