Recently, ontology embeddings representing entities in a low-dimensional space have been proposed for ontology completion. However, the ontology embeddings for concept subsumption prediction do not address the difficulties of similar and isolated entities and fail to extract the global information of annotation axioms from an ontology. In this paper, we propose a self-matching training method for the two ontology embedding models: Inverted-index Matrix Embedding (InME) and Co-occurrence Matrix Embedding (CoME). The two embeddings capture the global and local information in annotation axioms by means of the occurring locations of each word in a set of axioms and the co-occurrences of words in each axiom. The self-matching training method increases the robustness of the concept subsumption prediction when predicted superclasses are similar to subclasses and are isolated to other entities in an ontology. Our evaluation experiments show that the self-matching training method with InME outperforms the existing ontology embeddings for the GO and FoodOn ontologies and that the method with the concatenation of CoME and OWL2Vec* outperforms them for the HeLiS ontology.

翻译：近期，将本体实体表示为低维向量的本体嵌入方法已被提出用于本体补全任务。然而，现有用于概念包含关系预测的本体嵌入方法未能解决相似实体与孤立实体的识别难题，且难以从本体中提取注释公理的全局信息。本文提出一种面向两种本体嵌入模型的自匹配训练方法：倒排索引矩阵嵌入（InME）与共现矩阵嵌入（CoME）。这两种嵌入分别通过单词在公理集合中的出现位置及单词在每条公理中的共现关系，捕获注释公理中的全局与局部信息。自匹配训练方法增强了预测父类与子类相似或与本体中其他实体存在隔离关系时的概念包含关系预测鲁棒性。评估实验表明：基于InME的自匹配训练方法在GO本体和FoodOn本体上优于现有本体嵌入方法；而采用CoME与OWL2Vec*拼接的方法在HeLiS本体上表现更优。