Donglei Lu1, Yundong Sun2, Qinrui Dai2, Xiaofang Li3,*, Dongjie Zhu4, Haiwen Du2, Yansong Wang4, Rongning Qu3, Ning Cao1, Gregory M. P. O’Hare5
Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1141-1151, 2022, DOI:10.32604/iasc.2022.020218
Abstract The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct… More >
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