Raker: A Relation-aware Knowledge Reasoning Model for Inductive Relation Prediction

Code and data for paper Raker: A Relation-aware Knowledge Reasoning Model for Inductive Relation Prediction.

Requirements:

• huggingface transformer
• pytorch
• networkx
• tqdm
• numpy
• sklearn
• ipdb

Download the Dataset Split

Here we provide the data split used in paper in folder "data". The $DATASET$PART and $DATASET$PART_ind contain corresponding transductive and inductive subgraphs. Each train/valid/test file contains a list of knowledge graph triples. "ranking_head.txt" and "ranking_tail.txt" are presampled candidates for the predicting the missing tail triple and missing head triple in knowledge graph completion. Each triple contains 50 candidates for tail and 50 for head in this file. $DATASET denotes the dataset name, and $PART denotes the size of the dataset, whether it is a fewshot version or full. If $PART is not specified, it is full by default.

Preprocessing Data

folder "bertrl_data" provides an example of preprocessed data to be input of the model.

part paramerter can be specified as full, 1000, 2000, referring to folder "data".

python load_data.py -d $DATASET -st test --part full --hop 3 --ind_suffix "_ind" --suffix "_neg10_max_inductive"

Raker

1. Training model We provide example bash scripts in train.sh