Search Results for author: Yongqi Zhang

Found 21 papers, 16 papers with code

Efficient Hyper-parameter Search for Knowledge Graph Embedding

1 code implementation ACL 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.

AutoML Knowledge Graph Embedding

Knowledge-Enhanced Recommendation with User-Centric Subgraph Network

1 code implementation21 Mar 2024 Guangyi Liu, Quanming Yao, Yongqi Zhang, Lei Chen

Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences.

Collaborative Filtering Recommendation Systems

Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs

1 code implementation15 Mar 2024 Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han

To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query.

Knowledge Graphs Link Prediction

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions

1 code implementation13 Mar 2024 Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin, Ruifeng Xu

Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare.

Language Modelling Reinforcement Learning (RL)

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network

1 code implementation15 Nov 2023 Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng

Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.

Relation-aware Ensemble Learning for Knowledge Graph Embedding

2 code implementations13 Oct 2023 Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.

Ensemble Learning Knowledge Graph Embedding +1

Logical Expressiveness of Graph Neural Network for Knowledge Graph Reasoning

no code implementations22 Mar 2023 Haiquan Qiu, Yongqi Zhang, Yong Li, Quanming Yao

Our results first show that GNN can capture logical rules from graded modal logic, providing a new theoretical tool for analyzing the expressiveness of GNN for KG reasoning; and a query labeling trick makes it easier for GNN to capture logical rules, explaining why SOTA methods are mainly based on labeling trick.

AutoWeird: Weird Translational Scoring Function Identified by Random Search

no code implementations24 Jul 2022 Hansi Yang, Yongqi Zhang, Quanming Yao

This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score.

Attribute Knowledge Graphs +1

AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning

2 code implementations30 May 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han

An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step.

Knowledge Graphs

KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

2 code implementations5 May 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.

Graph Learning

Knowledge Graph Reasoning with Relational Digraph

3 code implementations13 Aug 2021 Yongqi Zhang, Quanming Yao

In this paper, we introduce a novel relational structure, i. e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG's local evidence.

Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding

3 code implementations22 Apr 2021 Shimin Di, Quanming Yao, Yongqi Zhang, Lei Chen

The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.

AutoML Knowledge Graph Embedding +2

Combining Self-Supervised and Supervised Learning with Noisy Labels

no code implementations16 Nov 2020 Yongqi Zhang, HUI ZHANG, Quanming Yao, Jun Wan

Thus, inspired by the observation that classifier is more robust to noisy labels while representation is much more fragile, and by the recent advances of self-supervised representation learning (SSRL) technologies, we design a new method, i. e., CS$^3$NL, to obtain representation by SSRL without labels and train the classifier directly with noisy labels.

Learning with noisy labels Representation Learning +1

Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding

1 code implementation24 Oct 2020 Yongqi Zhang, Quanming Yao, Lei Chen

In this paper, motivated by the observation that negative triplets with large gradients are important but rare, we propose to directly keep track of them with the cache.

Generative Adversarial Network Knowledge Graph Embedding

Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding

4 code implementations NeurIPS 2020 Yongqi Zhang, Quanming Yao, Lei Chen

In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths.

Knowledge Graph Embedding Neural Architecture Search

AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

3 code implementations26 Apr 2019 Yongqi Zhang, Quanming Yao, Wenyuan Dai, Lei Chen

The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.

AutoML Knowledge Graph Embedding +2

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

6 code implementations16 Dec 2018 Yongqi Zhang, Quanming Yao, Yingxia Shao, Lei Chen

Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.

Generative Adversarial Network Knowledge Graph Embedding +1

Automated Machine Learning: From Principles to Practices

1 code implementation31 Oct 2018 Zhenqian Shen, Yongqi Zhang, Lanning Wei, Huan Zhao, Quanming Yao

Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious.

BIG-bench Machine Learning Neural Architecture Search

XOGAN: One-to-Many Unsupervised Image-to-Image Translation

no code implementations18 May 2018 Yongqi Zhang

To learn the complex relationship between the two domains, we introduce an additional variable to control the variations in our one-to-many mapping.

Translation Unsupervised Image-To-Image Translation

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