1 code implementation • EMNLP 2021 • Chengjin Xu, Fenglong Su, Jens Lehmann
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs).
Ranked #2 on Entity Alignment on DICEWS-1K
no code implementations • EMNLP 2021 • Mojtaba Nayyeri, Chengjin Xu, Franca Hoffmann, Mirza Mohtashim Alam, Jens Lehmann, Sahar Vahdati
Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.
1 code implementation • 18 Mar 2024 • Yi Luo, Zhenghao Lin, Yuhao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
Subsequently, the retrieval model correlates new inputs with relevant guidelines, which guide LLMs in response generation to ensure safe and high-quality outputs, thereby aligning with human values.
no code implementations • 23 Feb 2024 • Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, HuaWei Shen, Yuanzhuo Wang
To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.
no code implementations • 2 Feb 2024 • Xuhui Jiang, Yuxing Tian, Fengrui Hua, Chengjin Xu, Yuanzhuo Wang, Jian Guo
Hallucinations in large language models (LLMs) are always seen as limitations.
no code implementations • 26 Dec 2023 • Zhengzhuo Xu, Sinan Du, Yiyan Qi, Chengjin Xu, Chun Yuan, Jian Guo
Multimodal Large Language Models (MLLMs) demonstrate impressive image understanding and generating capabilities.
no code implementations • 7 Oct 2023 • Xuhui Jiang, Chengjin Xu, Yinghan Shen, Xun Sun, Lumingyuan Tang, Saizhuo Wang, Zhongwu Chen, Yuanzhuo Wang, Jian Guo
Knowledge graphs (KGs) are structured representations of diversified knowledge.
3 code implementations • 15 Jul 2023 • Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, Jian Guo
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
no code implementations • 25 Jun 2023 • Haohan Zhang, Fengrui Hua, Chengjin Xu, Jian Guo, Hao Kong, Ruiting Zuo
The rapid advancement of Large Language Models (LLMs) has led to extensive discourse regarding their potential to boost the return of quantitative stock trading strategies.
no code implementations • 10 Apr 2023 • Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, Yong Dou
In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs).
1 code implementation • 7 Apr 2023 • Xuhui Jiang, Chengjin Xu, Yinghan Shen, Yuanzhuo Wang, Fenglong Su, Fei Sun, Zixuan Li, Zhichao Shi, Jian Guo, HuaWei Shen
Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios.
no code implementations • 11 Feb 2023 • Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, You Dou
Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting.
no code implementations • 1 Jun 2022 • Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
1 code implementation • NeurIPS 2023 • Xueyuan Lin, Chengjin Xu, Haihong E, Fenglong Su, Gengxian Zhou, Tianyi Hu, Ningyuan Li, Mingzhi Sun, Haoran Luo
In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between).
1 code implementation • 4 Mar 2022 • Chengjin Xu, Fenglong Su, Jens Lehmann
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs).
no code implementations • 18 Feb 2022 • Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann
In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations.
no code implementations • 29 Sep 2021 • Chengjin Xu, Fenglong Su, Jens Lehmann
Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data.
1 code implementation • NAACL 2021 • Chengjin Xu, Yung-Yu Chen, Mojtaba Nayyeri, Jens Lehmann
Representation learning approaches for knowledge graphs have been mostly designed for static data.
no code implementations • 11 Apr 2021 • Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann
For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.
no code implementations • 13 Oct 2020 • Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann, Sahar Vahdati
To this end, we represent each relation (edge) in a KG as a vector field on a smooth Riemannian manifold.
no code implementations • COLING 2020 • Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.
2 code implementations • COLING 2020 • Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann
We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.
1 code implementation • 18 Nov 2019 • Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann
Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.
no code implementations • 25 Sep 2019 • Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh, Hamed Shariat Yazdi, Jens Lehmann
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
no code implementations • 2 Sep 2019 • Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh, Hamed Shariat Yazdi, Jens Lehmann
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
no code implementations • 20 Aug 2019 • Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann, Hamed Shariat Yazdi
We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph.
Ranked #17 on Link Prediction on FB15k