Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs
Continuous Query Decomposition
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs
Update
We implemented CQD in the KGReasoning framework, a library from SNAP implementing several Complex Query Answering models, which also supports experimenting with the Query2Box and BetaE datasets (in this repo, we only consider the former). Our implementation is available at this link.
This repository contains the official implementation for our ICLR 2021 (Oral, Outstanding Paper Award) paper, Complex Query Answering with Neural Link Predictors:
@inproceedings{
arakelyan2021complex,
title={Complex Query Answering with Neural Link Predictors},
author={Erik Arakelyan and Daniel Daza and Pasquale Minervini and Michael Cochez},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=Mos9F9kDwkz}
}
In this work we present CQD, a method that reuses a pretrained link predictor to