Aligning Cross-Lingual Entities with Multi-Aspect Information
Figure 1: An example fragment of two KGs (in English and Japanese) connected by an inter-lingual link (ILL). In addition to the graph structures (top) consisting of entity nodes and typed relation edges, KGs also provide attributes and literal descriptions of entities (bottom).
['Descriptions The University of Toronto is a public research university in Toronto, Ontario, Canada ⋯', 'Descriptions トロント大学 は、オンタリオ州、トロントに本部を置くカナダの州立大学である ⋯']
A growing number of multilingual knowledge graphs (KGs) have been built, such as DBpedia Bizer et al. et al. Such KGs are connected via inter-lingual links (ILLs) Highly-integrated multilingual KGs contain useful knowledge that can benefit many knowledge-driven cross-lingual NLP tasks, such as machine translation Moussallem et al. However, the coverage of ILLs among existing KGs is quite low Chen et al. : for example, less than 20% of the entities in DBpedia are covered by ILLs. The goal of cross-lingual entity alignment is to discover entities from different monolingual KGs that actually refer to the same real-world entities, i.e., discovering the missing ILLs. Traditional methods for this task apply machine translation techniques to translate entity labels Spohr et al. The quality of alignments in the cross-lingual scenario heavily depends on the quality of the adopted translation systems. In addition to entity labels, existing KGs also provide multi-aspect information of entities, including topological connections, relation types, attributes, and literal descriptions expressed in different languages Bizer et al. ; Xie et al. The key challenge of addressing such a task thus is how to better model and use provided multi-aspect information of entities to bridge cross-lingual gaps and find more equivalent entities (i.e., ILLs). In addition, we also consider the relation and attribute features. Specifically, they share the same relation types, e.g., “country” and “almaMater”, and some attributes, e.g., “foundDate” and “創立年”. To capture relation information, Schlichtkrull et al. However, with respect to this task, existing KGs typically contain thousands of relation types but few pre-aligned ILLs. Directly adopting RGCN may introduce too many parameters for the limited training data and thus cause overfitting. Wang et al. They also considered attributes as input features in their architecture. However, this approach may lose information about relation types. Therefore, we regard relations and attributes of entities as bag-of-words features to explicitly model these two aspects. Specifically, we construct count-based N-hot vectors Xr and Xa for these two aspects of features, respectively, where the (i,j) entry is the count of the j-th relation (attribute) for the corresponding entity ei. Note that we only consider the top-F most frequent relations and attributes to avoid data sparsity issues. Thus, for each entity, both of its relation and attribute features are F-dimensional vectors.