TY - GEN
T1 - Two Birds with One Stone
T2 - 19th International Conference on Advanced Data Mining and Applications, ADMA 2023
AU - Pang, Jun
AU - Qin, Hong Chao
AU - Liu, Yan
AU - Liu, Xiao Qi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness); (2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD+, based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD+ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets.
AB - Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness); (2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD+, based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD+ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets.
KW - Knowledge hypergraph
KW - Link prediction
KW - Tensor Decomposition
UR - https://www.scopus.com/pages/publications/85177549789
U2 - 10.1007/978-3-031-46664-9_6
DO - 10.1007/978-3-031-46664-9_6
M3 - Conference contribution
AN - SCOPUS:85177549789
SN - 9783031466632
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 90
BT - Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
A2 - Yang, Xiaochun
A2 - Wang, Bin
A2 - Suhartanto, Heru
A2 - Wang, Guoren
A2 - Jiang, Jing
A2 - Li, Bing
A2 - Zhu, Huaijie
A2 - Cui, Ningning
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 August 2023 through 23 August 2023
ER -