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 topological structures in two entities. To choose the right neighbors, we apply a down-sampling process to select the most informative entities towards the central target entity from its one-hop and two-hop neighbors. To verify the effectiveness of matching this neighborhood with any neighborhood in the corresponding node, we give a cosine cross-graph neighborhood matching method and conduct detailed research and analysis on three entity matching datasets, which proves the effectiveness of MSM.
Highlights
Entity alignment is designed to determine whether two or more entities with different knowledge graphs point to the same object in the real world
We propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge
1 Introduction Entity alignment is designed to determine whether two or more entities with different knowledge graphs point to the same object in the real world
Summary
Entity alignment is designed to determine whether two or more entities with different knowledge graphs point to the same object in the real world. Entity alignment is widely used in graph networks and social networks [1,2]. The most advanced entity alignment solutions mainly rely on the structure information of knowledge map to judge the equivalence of entities, but in the real-world knowledge map, most entities only have low node degree and little structure information. The lack of annotated data greatly limits the effectiveness of the entity. Entity alignment is not trivial, because real-life knowledge graphs are often incomplete and different knowledge graphs typically have heterogeneous schemas and the equivalent entities in different graphs often have different neighborhood structures
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