This paper has been accpeted by 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
by Kunal Goyal, Utkarsh Gupta, Abir De and Soumen Chakrabarti
@inproceedings{10.1145/3397271.3401216,
author = {Goyal, Kunal and Gupta, Utkarsh and De, Abir and Chakrabarti, Soumen},
title = {Deep Neural Matching Models for Graph Retrieval},
year = {2020},
isbn = {9781450380164},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3397271.3401216},
doi = {10.1145/3397271.3401216},
abstract = {Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1701–1704},
numpages = {4},
keywords = {product graph, random walk, scoring subgraph match, graph search},
location = {Virtual Event, China},
series = {SIGIR '20}
}
Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.
- Python 3.6
- tensorflow 1.15.0
pip install -r requirements.txt
Checkpoints are available in checkpoints/
You have to download the SQuAD 2.0 dataset.
python vaq_dataset_gen.py --data_path $CLEVR_{NOISE} --noise $NOISEAlso available in data/clevr/
python squaddep.py --squad ./data/squad/train-v2.0.json --data ./data/squad/squad.pklavailable in data/squad
python train.py --data_path ./data/clevr/clevr_{$NOISE}.pkl --name clevr_{$NOISE} --dataset clevr --logfile clevr_noise_{$NOISE}.log --num_queries 50 --num_walks 15 --max_length_walk 15 --sparse_walk True --walk_method random
python train.py --data_path ./data/squad/squad.pkl --name squad --dataset squad --num_queries_train 1000 --num_queries_eval 100 --logfile ./logs/squad.log --early_stopping 10 --delta 0.2 --num_walks 16 --max_length_walk 16 --nlayer1 32 --nlayer2 32 --elayer1 8 --elayer2 8 --nelayer1 32 --nelayer2 32 --walk_method random --sparse_walk True