# DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2306.10290 · HTML: https://mlsanity.com/paper/2306.10290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs](https://mlsanity.com/paper/1905.04914/) (2019)
- [A Re-evaluation of Knowledge Graph Completion Methods](https://mlsanity.com/paper/1911.03903/) (2019)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions](https://mlsanity.com/paper/1911.00219/) (2019)
- [Interaction Embeddings for Prediction and Explanation in Knowledge Graphs](https://mlsanity.com/paper/1903.04750/) (2019)
- [CoKE: Contextualized Knowledge Graph Embedding](https://mlsanity.com/paper/1911.02168/) (2019)
- [Multi-Task Learning for Dense Prediction Tasks: A Survey](https://mlsanity.com/paper/2004.13379/) (2020)
- [Neural Generative Question Answering](https://mlsanity.com/paper/1512.01337/) (2015)
- [Probabilistic Reasoning via Deep Learning: Neural Association Models](https://mlsanity.com/paper/1603.07704/) (2016)

## Abstract (arXiv)

To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Networks (GCN). However, we observe that entity embeddings aggregated from neighbors in different directions are just simply averaged to complete single-tasks by existing GCN based models, ignoring the specific requirements of forward and backward sub-tasks. In this paper, we propose a Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction information, the multi-head self-attention is applied to specifically combine embeddings in different directions based on various entities and sub-tasks, the geometric constraints are imposed to adjust the distribution of embeddings, and the traditional binary cross-entropy loss is modified to reflect the triple uncertainty. Moreover, the competitive experiments results on several benchmark datasets verify the effectiveness of our model.