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Improve the translational distance models for knowledge graph embedding

机译：改善知识图形嵌入的平移距离模型

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摘要

Knowledge graph embedding techniques can be roughly divided into two mainstream, translational distance models and semantic matching models. Though intuitive, translational distance models fail to deal with the circle structure and hierarchical structure in knowledge graphs. In this paper, we propose a general learning framework named TransX-pa, which takes various models (TransE, TransR, TransH and TransD) into consideration. From this unified viewpoint, we analyse the learning bottlenecks are: (i) the common assumption that the inverse of a relation r is modelled as its opposite - r; and (ii) the failure to capture the rich interactions between entities and relations. Correspondingly, we introduce position-aware embeddings and self-attention blocks, and show that they can be adapted to various translational distance models. Experiments are conducted on different datasets extracted from real-world knowledge graphs Freebase and WordNet in the tasks of both triplet classification and link prediction. The results show that our approach makes a great improvement, showing a better, or comparable, performance with state-of-the-art methods.
机译：知识图形嵌入技术可以大致分为两个主流，翻译距离模型和语义匹配模型。虽然直观，平移距离模型无法处理知识图中的圆形结构和分层结构。在本文中，我们提出了一个名为Transx-Pa的一般学习框架，它考虑了各种型号（Transe，Transr，Transh和TransD）。从这个统一的观点来看，我们分析了学习瓶颈是：（i）关系R的倒数r的共同假设是与其对面建模的; （ii）未能捕捉实体与关系之间丰富的相互作用。相应地，我们引入了位置感知嵌入和自我关注块，并表明它们可以适应各种翻译距离模型。在基于Triplet分类和链路预测的任务中，在从真实的知识图中提取的不同数据集上进行实验。结果表明，我们的方法具有巨大的改进，呈现出更好或可比性，性能与最先进的方法。

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