1 code implementation • 22 May 2023 • Yuxin Jiang, Chunkit Chan, Mingyang Chen, Wei Wang
The practice of transferring knowledge from a sophisticated, closed-source large language model (LLM) to a compact, open-source LLM has garnered considerable attention.
1 code implementation • 28 Apr 2023 • Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.
1 code implementation • 23 Apr 2023 • Yichi Zhang, Mingyang Chen, Wen Zhang
Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training.
1 code implementation • 3 Mar 2023 • Wen Zhang, Yushan Zhu, Mingyang Chen, Yuxia Geng, Yufeng Huang, Yajing Xu, Wenting Song, Huajun Chen
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
1 code implementation • 3 Feb 2023 • Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, Huajun Chen
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
no code implementations • 3 Feb 2023 • Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen
In this paper, we use a set of general terminologies to unify these methods and refer to them as Knowledge Extrapolation.
1 code implementation • 3 Jan 2023 • Zhen Yao, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, Huajun Chen
And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.
1 code implementation • CVPR 2023 • Bo Huang, Mingyang Chen, Yi Wang, Junda Lu, Minhao Cheng, Wei Wang
Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization.
1 code implementation • 20 Oct 2022 • Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, Xinliang Wu, Yi Yang, Mingyi Chen, Zhaoyang Lian, YingYing Li, Lei Cheng, Huajun Chen
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
1 code implementation • 8 Oct 2022 • Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan, Mingyang Chen, Song Jiang, Wen Zhang, Huajun Chen
Subgraph reasoning with message passing is a promising and popular solution.
1 code implementation • 10 May 2022 • Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, Huajun Chen
We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.
1 code implementation • 25 Feb 2022 • Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, Zonggang Yuan, Feiyu Xiong, Huajun Chen
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
no code implementations • 16 Dec 2021 • Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen
We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.
1 code implementation • 27 Oct 2021 • Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen
In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.
no code implementations • 21 Oct 2021 • Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Qiang Zhang, Huajun Chen
Embedding-based entity alignment (EEA) has recently received great attention.
no code implementations • 29 Sep 2021 • Wen Zhang, Mingyang Chen, Zezhong Xu, Yushan Zhu, Huajun Chen
KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.
no code implementations • 1 Jan 2021 • Lingbing Guo, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen
In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.
2 code implementations • 24 Oct 2020 • Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, Huajun Chen
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
no code implementations • 13 Sep 2020 • Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei zhang, Huajun Chen
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
2 code implementations • CVPR 2020 • Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, Cewu Lu
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on Human-Object Interaction Detection on HICO
1 code implementation • IJCNLP 2019 • Mingyang Chen, Wen Zhang, Wei zhang, Qiang Chen, Huajun Chen
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.
2 code implementations • 13 Apr 2019 • Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Mingyang Chen, Ze Ma, Shiyi Wang, Hao-Shu Fang, Cewu Lu
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on Human-Object Interaction Detection on HICO (using extra training data)