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.

本文提出了一个自匹配训练方法，对本体嵌入模型进行训练，包括倒排索引矩阵嵌入（InME）和共现矩阵嵌入（CoME），以捕捉本体注释公理中的全局和局部信息。通过自匹配训练方法，在预测超类与子类相似且与本体中其他实体孤立的情况下，可以提高概念包含预测的鲁棒性。实验证实，在 GO 和 FoodOn 本体中，使用 InME 的自匹配训练方法优于现有的本体嵌入方法；而在 HeLiS 本体中，使用 CoME 和 OWL2Vec * 的连接方法优于其他方法。