Abstract
Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models’ supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9%across all experiment datasets. The code is available at https://github.com/ChenBaiyang/CAREA.
Highlights
Knowledge graphs (KGs) that possess machine-readable representations of factual knowledge are becoming the basis for many applications such as web search (Google and Bing), recommendations (Amazon and eBay), and social networks (Facebook and Linkedin)
Multilingual KGs (e.g., DBpedia [1], YAGO [2], and ConceptNet [3]) are constructed in separate languages from various data sources and contain a wealth of complementary facts. e bridging of language gaps and the improvement of user experience from downstream cross-language applications benefit a lot from the entity equivalent in multilingual KGs
Most existing entity alignment methods entirely rely on the graph structures, while the abundant attribute information in KGs remains unexplored. e attributes of an entity represented by different languages often share enormous semantic information, leading to a potentially valid view of the entities connected to multilanguage KGs
Summary
Knowledge graphs (KGs) that possess machine-readable representations of factual knowledge are becoming the basis for many applications such as web search (Google and Bing), recommendations (Amazon and eBay), and social networks (Facebook and Linkedin). Discrete Dynamics in Nature and Society multilingual KGs, as the entity attributes and graph structure information naturally form two independent views of a KG. Is paper introduces a cotraining based approach CAREA to learn embeddings from two independent views of knowledge (relationships and attributes) in multilingual KGs. CAREA iteratively trains two-component models that are called attribute-based model fattr and structure-based model fstruc, respectively. Fstruc adopts a graph attention mechanism to capture the multirelation characteristics of KGs. During each iteration of the cotraining process, both models alternately predict a set of new potential aligned entity pairs to strengthen the supervision of cross-lingual learning. During each iteration of the cotraining process, both models alternately predict a set of new potential aligned entity pairs to strengthen the supervision of cross-lingual learning Such collaborative predictions gradually improve the performance of each model.
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