1 code implementation • 30 Nov 2022 • Guanglin Niu, Bo Li
To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense.
1 code implementation • ACL 2022 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu
The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance.
no code implementations • COLING 2022 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models.
no code implementations • 29 Oct 2021 • Guanglin Niu, Yang Li, Chengguang Tang, Zhongkai Hu, Shibin Yang, Peng Li, Chengyu Wang, Hao Wang, Jian Sun
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering Knowledge Graph Embedding +1
1 code implementation • ACL 2021 • Shan Yang, Yongfei Zhang, Guanglin Niu, Qinghua Zhao, ShiLiang Pu
Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.
1 code implementation • 27 Apr 2021 • Guanglin Niu, Yang Li, Chengguang Tang, Ruiying Geng, Jian Dai, Qiao Liu, Hao Wang, Jian Sun, Fei Huang, Luo Si
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
no code implementations • 6 Oct 2020 • Guanglin Niu, Bo Li, Yongfei Zhang, Yongpan Sheng, Chuan Shi, Jingyang Li, ShiLiang Pu
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu, Jingyang Li
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.
1 code implementation • 20 Nov 2019 • Guanglin Niu, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, Xiaowei Zhang
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.