The mainstream approach to the development of ontologies is merging ontologies encoding different information, where one of the major difficulties is that the heterogeneity motivates the ontology merging but also limits high-quality merging performance. Thus, the entity type (etype) recognition task is proposed to deal with such heterogeneity, aiming to infer the class of entities and etypes by exploiting the information encoded in ontologies. In this paper, we introduce a property-based approach that allows recognizing etypes on the basis of the properties used to define them. From an epistemological point of view, it is in fact properties that characterize entities and etypes, and this definition is independent of the specific labels and hierarchical schemas used to define them. The main contribution consists of a set of property-based metrics for measuring the contextual similarity between etypes and entities, and a machine learning-based etype recognition algorithm exploiting the proposed similarity metrics. Compared with the state-of-the-art, the experimental results show the validity of the similarity metrics and the superiority of the proposed etype recognition algorithm.

翻译：本体论发展的主流方法是合并编码不同信息的本体，其中主要难点在于异质性虽推动了本体合并，却也限制了高质量合并性能。为此，提出实体类型识别任务以处理此类异质性，旨在通过挖掘本体编码的信息推断实体类别与实体类型。本文提出一种基于属性的方法，能够根据定义实体类型时使用的属性进行识别。从认识论角度看，正是属性刻画了实体与实体类型，且这种定义独立于用于定义它们的特定标签与层级模式。主要贡献包括：一套用于度量实体类型与实体之间上下文相似性的属性相似度指标，以及一种利用所提相似度指标的基于机器学习的实体类型识别算法。与现有最优方法相比，实验结果表明了相似度指标的有效性以及所提实体类型识别算法的优越性。