End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

11/11/2018
by   Chao Shang, et al.
4

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end Structure-Aware Convolutional Networks (SACN) that take the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and relation types. It has learnable weights that collect adaptive amount of information from neighboring graph nodes, resulting in more accurate embeddings of graph nodes. In addition, the node attributes are added as the nodes and are easily integrated into the WGCN. The decoder Conv-TransE extends the state-of-the-art ConvE to be translational between entities and relations while keeps the state-of-the-art performance as ConvE. We demonstrate the effectiveness of our proposed SACN model on standard FB15k-237 and WN18RR datasets, and present about 10 improvement over the state-of-the-art ConvE in terms of HITS@1, HITS@3 and HITS@10.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/11/2020

Rediscovering alignment relations with Graph Convolutional Networks

Knowledge graphs are concurrently published and edited in the Web of dat...
research
03/04/2022

R-GCN: The R Could Stand for Random

The inception of Relational Graph Convolutional Networks (R-GCNs) marked...
research
05/21/2019

Joint embedding of structure and features via graph convolutional networks

The creation of social ties is largely determined by the entangled effec...
research
07/10/2023

Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks

The graph identification problem consists of discovering the interaction...
research
03/08/2019

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

In this paper, we propose a deep reinforcement learning framework called...
research
09/03/2019

Semantically-Regularized Logic Graph Embeddings

In this work, we aim to utilize prior knowledge encoded as logical rules...
research
09/03/2019

Embedding Symbolic Knowledge into Deep Networks

In this work, we aim to leverage prior symbolic knowledge to improve the...

Please sign up or login with your details

Forgot password? Click here to reset