2 code implementations • 10 Apr 2024 • Mikhail Galkin, Jincheng Zhou, Bruno Ribeiro, Jian Tang, Zhaocheng Zhu
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations.
no code implementations • 10 Oct 2023 • Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, Hanjun Dai
In the deduction stage, the LLM is then prompted to employ the learned rule library to perform reasoning to answer test questions.
1 code implementation • 6 Oct 2023 • Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
1 code implementation • 6 Oct 2023 • Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, Zhaocheng Zhu
The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies.
no code implementations • 2 Oct 2023 • Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, Jian Tang
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
1 code implementation • 26 Mar 2023 • Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, Jure Leskovec
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
1 code implementation • 13 Oct 2022 • Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang
Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
2 code implementations • NeurIPS 2023 • Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin, Sophie Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang
Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration.
Ranked #10 on Link Property Prediction on ogbl-wikikg2
1 code implementation • 5 Jun 2022 • Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
1 code implementation • ICML 2022 • Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
Ranked #3 on Complex Query Answering on FB15k-237
1 code implementation • 16 Feb 2022 • Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
1 code implementation • NeurIPS 2021 • Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, Jian Tang
To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.
Ranked #1 on Link Prediction on FB15k-237
1 code implementation • ICLR 2020 • Chence Shi, Minkai Xu, Zhaocheng Zhu, Wei-Nan Zhang, Ming Zhang, Jian Tang
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
Ranked #1 on Molecular Graph Generation on MOSES
1 code implementation • 13 Nov 2019 • Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.
no code implementations • 20 Oct 2019 • Jinting Chen, Zhaocheng Zhu, Cheng Li, Yuming Zhao
Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel.
1 code implementation • 2 Mar 2019 • Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang
In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.
Ranked #1 on Node Classification on YouTube
no code implementations • 16 Nov 2018 • Conghui Li, Zhaocheng Zhu, Yuming Zhao
However, due to the limitation in data sources and the subjectiveness in pain intensity values, it is hard to adopt modern deep neural networks for this problem without domain-specific auxiliary design.
no code implementations • 5 Jul 2017 • Zhaocheng Zhu, Junfeng Hu
Recently, doc2vec has achieved excellent results in different tasks.