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Attribute acquisition for classes is a key step in ontology construction,which is often achieved by community members manually. This paper investigatesan attention-based automatic paradigm called TransATT for attributeacquisition, by learning the representation of hierarchical classes andattributes in Chinese ontology. The attributes of an entity can be acquired bymerely inspecting its classes, because the entity can be regard as the instanceof its classes and inherit their attributes. For explicitly describing of theclass of an entity unambiguously, we propose class-path to represent thehierarchical classes in ontology, instead of the terminal class word of thehypernym-hyponym relation (i.e., is-a relation) based hierarchy. The highperformance of TransATT on attribute acquisition indicates the promisingability of the learned representation of class-paths and attributes. Moreover,we construct a dataset named \textbf{BigCilin11k}. To the best of ourknowledge, this is the first Chinese dataset with abundant hierarchical classesand entities with attributes.

Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes