Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

Zhanqiu Zhang, Jianyu Cai, Jie Wang

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

Repositories linked to this paper

• MIRALab-USTC/KGE-DURA — marked official in Papers with Code; named in the paper
• MIRALab-USTC/KGE-HAKE
• miralab-ustc/qe-cone

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

Abstract

Tensor factorization based models have shown great power in knowledge graph completion (KGC). However, their performance usually suffers from the overfitting problem seriously. This motivates various regularizers-such as the squared Frobenius norm and tensor nuclear norm regularizers-while the limited applicability significantly limits their practical usage. To address this challenge, we propose a novel regularizer-namely, DUality-induced RegulArizer (DURA)-which is not only effective in improving the performance of existing models but widely applicable to various methods. The major novelty of DURA is based on the observation that, for an existing tensor factorization based KGC model (primal), there is often another distance based KGC model (dual) closely associated with it. Experiments show that DURA yields consistent and significant improvements on benchmarks.

For agents

The same record, over MCP at https://syntology.ai/mcp:

get_harvested_code_for_paper("2011.05816")
get_code_for_paper("2011.05816")
have("2011.05816")

Connect an agent — have() is free.