A Vectorized Relational Graph Convolutional Network for Multi-Relational
Network Alignment
Abstract
Alignment of multiple multi-relational networks,
such as knowledge graphs, is vital for AI applications. Different from the conventional alignment
models, we apply the graph convolutional network
(GCN) to achieve more robust network embedding
for the alignment task. In comparison with existing
GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph
convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role
discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multirelational network alignment tasks. Anchors are
used to supervise the objective function which aims
at minimizing the distances between anchors, and
to generate new cross-network triplets to build a
bridge between different knowledge graphs at the
level of triplet to improve the performance of alignment. Experiments on real-world datasets show
that the proposed solutions outperform the stateof-the-art methods in terms of network embedding,
entity alignment, and relation alignment