Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
Source abstract
Abstract
In recent years, the scale of knowledge graphs and the number of entities have grown rapidly. Entity matching across different knowledge graphs has become an urgent problem to be solved for knowledge fusion. With the importance of entity matching being increasingly evident, the use of representation learning technologies to find matched entities has attracted extensive attention due to the computability of vector representations. However, existing studies on representation learning technologies cannot make full use of knowledge graph relevant multi-modal information. In this paper, we propose a new cross-lingual entity matching method (called CLEM) with knowledge graph representation learning on rich multi-modal information. The core is the multi-view intact space learning method to integrate embeddings of multi-modal information for matching entities. Experimental results on cross-lingual datasets show the superiority and competitiveness of our proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Data Quality and Management · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
School of Computer and Software, Hangzhou Dianzi University, Hangzhou 310018, China
Funding
NSFC · Project for the Doctor of Entrepreneurship and Innovation in Jiangsu Province · Fundamental Research Funds for the Central Universities
References (39)
Show source-provided references
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