Research Area:  Machine Learning

Abstract:

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal knowledge graph embedding framework, to address entity alignment. We first devise an early fusion method for fusing features of multi-modal entity representations of a KG. Simultaneously, multiple representations of various types of knowledge are generated independently by various techniques and fused by a low-rank multi-modal late fusion method. Finally, the outputs of early and late fusion methods are combined using a dual fusion scheme. DFMKE provides an ultimate fusion solution by leveraging the advantages of early and late fusion methods. Extensive experiments on two public datasets show that the DFMKE outperforms state-of-the-art methods by a significant margin achieving at least 10% more regard to Hits@n and MRR metrics.

Keywords:  

Author(s) Name:  Jia Zhu, Changqin Huang, Pasquale De Meo

Publisher name:  ACM Digital Library

DOI:  10.1016/j.inffus.2022.09.012

Volume Information:  Volume 90, Pages 111-119, (2023)

Paper Link:   https://dl.acm.org/doi/abs/10.1016/j.inffus.2022.09.012