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

## TL;DR

This paper introduces TransATT, an attention-based model for automatic attribute acquisition in Chinese ontologies, utilizing class-path representations to improve accuracy and constructing a new large-scale dataset for evaluation.

## Contribution

It proposes a novel class-path based representation learning method and a new Chinese dataset for attribute acquisition in ontology construction.

## Key findings

- TransATT achieves high performance in attribute acquisition tasks.
- Class-path representations outperform traditional terminal class methods.
- The BigCilin11k dataset provides a valuable resource for Chinese ontology research.

## Abstract

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.