论文标题
OWL2VEC*：猫头鹰本体论的嵌入
OWL2Vec*: Embedding of OWL Ontologies
论文作者
论文摘要
知识图的语义嵌入已被广泛研究并用于跨各个领域的预测和统计分析任务，例如自然语言处理和语义网络。但是，人们对开发嵌入猫头鹰（Web本体语言）本体的鲁棒方法的关注较少，这些方法可以表达出比知识图更广泛的语义范围，并且已在诸如生物信息学之类的领域中广泛采用。在本文中，我们提出了一个名为OWL2VEC*的本体嵌入方法的随机步行和单词嵌入，该方法通过考虑其图形结构，词汇信息和逻辑构造函数来编码OWL本体论的语义。我们对三个现实世界数据集的经验评估表明，OWL2VEC*从集体成员预测和班级吸收预测任务中本体学的这三个不同方面受益。此外，OWL2VEC*通常会显着优于我们实验中最新方法。
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies which can express a much wider range of semantics than knowledge graphs and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments.
