Learning Dynamic Knowledge Graph Embedding in Evolving Service Ecosystems via Meta-Learning

Hongliang Sun, Jinlan Liu, Can Wang, Dianbo Sui, Zhiying Tu, Xiaofei Xu

Abstract: In the context of dynamic service ecosystems, the inability of conventional knowledge graph embedding (KGE) methods to efficiently update incremental knowledge poses a significant challenge for the effectiveness of intelligent web applications. To address the continuous updating challenges of service knowledge, this paper introduces MetaHG, a meta-learning strategy for KGE. Unlike existing meta-learning KGE studies that focus solely on local entity information, MetaHG incorporates both local and potential global structural information from current snapshot’s seen knowledge graphs (KGs) to mitigate issues such as spatial deformation and enhance the representation of unseen entities. Our approach initializes entity embeddings using ‘in’ and ‘out’ relationship matrices and refines them through a hybrid graph neural network (GNN) framework, which includes a GNN layer for local information and a hypergraph neural network (HGNN) layer for potential global information. The meta-learning strategy embedded in MetaHG effectively transfers meta-knowledge for the accurate representation of emerging entities. Extensive experiments are conducted on a self-collected clothing industry service dataset and two publicly available open-source KG datasets. By comparing with several baselines, experiment results demonstrate the superior performance of MetaHG in generating high-quality embeddings for emerging entities and dynamically updating service knowledge.