OWL2Vec*: embedding of OWL ontologies
OWL2Vec*: embedding of OWL ontologies
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DOI：
10.1007/s10994-021-05997-6
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发表时间：
2021-06-16
期刊：
影响因子：
7.5
通讯作者：
Horrocks, Ian
中科院分区：
文献类型：
--
作者：
Chen, Jiaoyan;Hu, Pan;Horrocks, Ian
知识图的语义嵌入已被广泛研究，并用于跨自然语言处理和语义Web等各个领域的预测和统计分析任务。然而，很少有人注意到开发鲁棒的方法嵌入OWL（Web本体语言）本体，其中包含更丰富的语义信息比普通的知识图，并已被广泛采用的领域，如生物信息学。本文提出了一种基于随机游走和词嵌入的本体嵌入方法OWL2Vec*，该方法通过考虑图结构、词汇信息和逻辑构造函数对OWL本体的语义进行编码。我们对三个真实的世界数据集的实证评估表明，OWL2Vec* 在类成员预测和类包含预测任务中受益于本体的这三个不同方面。此外，在我们的实验中，OWL2Vec* 通常显著优于最先进的方法。
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 contain richer semantic information than plain 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.