4.8k citations · 4.8k across the 9 of their papers we have counts for
6 papers · 1 filter
Collaborative Policy Learning for Open Knowledge Graph Reasoning
Cong Fu, Tong Chen, Meng Qu +2
In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse an…
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
Meng Qu, Jian Tang, Yoshua Bengio
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large nu…
Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian Tang
Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely ex…
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Fan-Yun Sun, Meng Qu, Jordan Hoffmann +2
This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respec…
GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian Tang
This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in…
Learning Dual Retrieval Module for Semi-supervised Relation Extraction
Hongtao Lin, Jun Yan, Meng Qu +1
Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision---where only a limited number o…