Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes
This work addresses the manual bottleneck in ontology construction for Chinese language applications, offering an incremental improvement with a novel dataset.
The paper tackles the problem of automatic attribute acquisition for classes in ontology construction by proposing TransATT, an attention-based method that learns representations of hierarchical classes and attributes, achieving high performance on a new Chinese dataset called BigCilin11k.
Attribute acquisition for classes is a key step in ontology construction, which is often achieved by community members manually. This paper investigates an attention-based automatic paradigm called TransATT for attribute acquisition, by learning the representation of hierarchical classes and attributes in Chinese ontology. The attributes of an entity can be acquired by merely inspecting its classes, because the entity can be regard as the instance of its classes and inherit their attributes. For explicitly describing of the class of an entity unambiguously, we propose class-path to represent the hierarchical classes in ontology, instead of the terminal class word of the hypernym-hyponym relation (i.e., is-a relation) based hierarchy. The high performance of TransATT on attribute acquisition indicates the promising ability of the learned representation of class-paths and attributes. Moreover, we construct a dataset named \textbf{BigCilin11k}. To the best of our knowledge, this is the first Chinese dataset with abundant hierarchical classes and entities with attributes.