Relation Representation Learning for Special Cargo Ontology

Abstract

Non-transparent shipping processes of transporting goods with special handling needs (special cargoes) have resulted in inefficiency in the airfreight industry. Special cargo ontology elicits, structures, and stores domain knowledge and represents the domain concepts and relationship between them in a machine-readable format. In this paper, we proposed an ontology population pipeline for the special cargo domain, and as part of the ontology population task, we investigated how to build an efficient information extraction model from low-resource domains based on available domain data for industry use cases. For this purpose, a model is designed for extracting and classifying instances of different relation types between each concept pair. The model is based on a relation representation learning approach built upon a Hierarchical Attention-based Multi-task architecture in the special cargo domain. The results of experiments show that the model could represent the complex semantic information of the domain, and tasks initialized with these representations achieve promising results.