TaxoTrans: Taxonomy-Guided Entity Translation

Zhuliu Li, Yiming Wang, Xiao Yan, Weizhi Meng, Yanen Li, Jaewon Yang

Abstract: Taxonomies describe the definitions of entities, entities' attributes and the relations among the entities, and thus play an important role in building a knowledge graph. In this paper, we tackle the task of taxonomy entity translation, which is to translate the names of taxonomy entities in a source language to a target language. The translations then can be utilized to build a knowledge graph in the target language. Despite its importance, taxonomy entity translation remains a hard problem for AI models due to two major challenges. One challenge is understanding the semantic context in very short entity names. Another challenge is having deep understanding for the domain where the knowledge graph is built.We present TaxoTrans, a novel method for taxonomy entity translation that can capture the context in entity names and the domain knowledge in taxonomy. To achieve this, TaxoTrans creates a heterogeneous graph to connect entities, and formulates the entity name translation problem as link prediction in the heterogeneous graph: given a pair of entity names across two languages, TaxoTrans applies a graph neural network to determine whether they form a translation pair or not. Because of this graph, TaxoTrans can capture both the semantic context and the domain knowledge. Our offline experiments on LinkedIn's skill and title taxonomies show that by modeling semantic information and domain knowledge in the heterogeneous graph, TaxoTrans outperforms the state-of-the-art translation methods by ∼10%. Human annotation and A/B test results further demonstrate that the accurately translated entities significantly improves user engagements and advertising revenue at LinkedIn.