Abstract
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.
Highlights
Multilingual knowledge graphs (KGs), such as DBpedia (Auer et al, 2007) and Yago (Suchanek et al, 2007), represent human knowledge in the structured format and have been successfully used in many natural language processing applications
We report results of an ablation of our model that uses GCN1 to derive the two topic entity embeddings and directly feeds them to the prediction layer without using matching layer
We can see that even without considering any KG structural information, the BASELINE significantly outperforms previous works that mainly learn entity embeddings from the KG structure, indicating that the surface form is an important feature for the KG alignment task
Summary
Multilingual knowledge graphs (KGs), such as DBpedia (Auer et al, 2007) and Yago (Suchanek et al, 2007), represent human knowledge in the structured format and have been successfully used in many natural language processing applications. These KGs encode rich monolingual knowledge but lack the cross-lingual links to bridge the language gap. Since some entities in different languages may have different KG
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