Corpus Fusion and Text Summarization Extraction for Multi-Feature Enhanced Entity Alignment
Gang Liu, Wenli Yang, Tongli Wang, He Zhihao · ACM Transactions on Asian and Low-Resource Language Information Processing · 2025
Cross-lingual entity alignment endeavors to identify semantically similar entities within a knowledge graph, facilitating knowledge complementarity and enriching cross-lingual knowledge. In the context of knowledge-driven tasks such as cross-lingual question answering and knowledge recommendation, cross-lingual entity alignment can effectively enhancing the performance of these applications built upon cross-lingual knowledge graphs. However, the current methodologies exhibit constraints in efficiently extracting and combining features of multiple entities, rendering them unable to fully harness the wealth of extensive information provided by the knowledge graph. To address this challenge, we propose CFSE, a novel multi-feature enhanced fusion model, which includes deep extraction of complex entity relationship, name, and attribute features. Complex entity relationship features are extracted based on corpus fusion and RotatE model. Additionally, an algorithm based on BERT for multilingual text summarization was introduced to extract entity name and attribute features. Through comprehensive entity feature extraction, CFSE not only further improves the alignment accuracy, but also helps to maximize the depth mining of knowledge graph information. The effectiveness of CFSE in cross-lingual entity alignment applications was demonstrated through experimental results on the DBP15K dataset.