Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
Published 1 January 2019Open access
Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat‐Seng Chua
Citations175
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TL;DR
This paper proposes a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG), which can make better use of seed alignments to propagate over the entire graphs with KG-based constraints.
Abstract
Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Keywords
Computer Science
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