1 code implementation • 24 May 2022 • Huarui He, Jie Wang, Zhanqiu Zhang, Feng Wu
To tackle these problems, we propose a novel Adversarial Knowledge Distillation framework for graph models named GraphAKD, which adversarially trains a discriminator and a generator to adaptively detect and decrease the discrepancy.
no code implementations • 24 Mar 2022 • Jie Wang, Zhanqiu Zhang, Zhihao Shi, Jianyu Cai, Shuiwang Ji, Feng Wu
Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE).
2 code implementations • 8 Feb 2022 • Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu
Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.
1 code implementation • NeurIPS 2021 • Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu
To address this challenge, we propose a novel query embedding model, namely Cone Embeddings (ConE), which is the first geometry-based QE model that can handle all the FOL operations, including conjunction, disjunction, and negation.
no code implementations • 12 Jul 2021 • Jianyu Cai, Jiajun Chen, Taoxing Pan, Zhanqiu Zhang, Jie Wang
To address this challenge, we propose a framework that integrates three components -- a basic model ComplEx-CMRC, a rule miner AMIE 3, and an inference model to predict missing links.
3 code implementations • NeurIPS 2020 • Zhanqiu Zhang, Jianyu Cai, Jie Wang
Tensor factorization based models have shown great power in knowledge graph completion (KGC).
Ranked #2 on Link Prediction on YAGO3-10
9 code implementations • 21 Nov 2019 • Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
Ranked #1 on Knowledge Graph Completion on WN18RR