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。

本体包含关系预测的自匹配训练方法与注释嵌入模型

A Self-matching Training Method with Annotation Embedding Models for  Ontology Subsumption Prediction

Motivation: Ontologies are widely used in biology for data annotation,
integration, and analysis. In addition to formally structured axioms,
ontologies contain meta-data in the form of annotation axioms which provide
valuable pieces of information that characterize ontology classes. Annotations
commonly used in ontologies include class labels, descriptions, or synonyms.
Despite being a rich source of semantic information, the ontology meta-data are
generally unexploited by ontology-based analysis methods such as semantic
similarity measures. Results: We propose a novel method, OPA2Vec, to generate
vector representations of biological entities in ontologies by combining formal
ontology axioms and annotation axioms from the ontology meta-data. We apply a
Word2Vec model that has been pre-trained on PubMed abstracts to produce feature
vectors from our collected data. We validate our method in two different ways:
first, we use the obtained vector representations of proteins as a similarity
measure to predict protein-protein interaction (PPI) on two different datasets.
Second, we evaluate our method on predicting gene-disease associations based on
phenotype similarity by generating vector representations of genes and diseases
using a phenotype ontology, and applying the obtained vectors to predict
gene-disease associations. These two experiments are just an illustration of
the possible applications of our method. OPA2Vec can be used to produce vector
representations of any biomedical entity given any type of biomedical ontology.
Availability: this https URL Contact:
robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.

该论文提出了一种新的方法，OPA2Vec，通过将正式本体公理和本体元数据中的注释公理结合起来，生成生物实体在本体中的向量表示，从而进行语义相似度计算。