Zongwei Liang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zongwei Liang has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Hui Liu
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications - Junan Yang
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications - Keju Huang
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications - Lin Cui
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications - Lingzhi Qu
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications - Xiang Li
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
2 shared publications
Latest Publications
- Open Access
- 28 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (7) , 1058
SeAttE: An Embedding Model Based on Separating Attribute Space for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models.
- 28 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (7) , 1058
Open AccessSeAttE: An Embedding Model Based on Separating Attribute Space for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models.
- Open Access
- 15 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (6) , 908
HRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion
Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets.- 15 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (6) , 908
Open AccessHRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion
Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets. - Open Access
- 1 January 2021
- Published by Association for Computational Linguistics (ACL)
- p. 7920-7929
A Semantic Filter Based on Relations for Knowledge Graph Completion
Zongwei Liang, Junan Yang, Hui Liu, Keju Huang. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
- 1 January 2021
- Published by Association for Computational Linguistics (ACL)
- p. 7920-7929
Open AccessA Semantic Filter Based on Relations for Knowledge Graph Completion
Zongwei Liang, Junan Yang, Hui Liu, Keju Huang. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.