A multi-strategy approach for the merging of multiple taxonomies

• Published in: Journal of information science 2022-06, Vol.48 (3), p.283-303
• Main Authors: Chen, Mao, Wu, Chao, Yang, Zongkai, Liu, Sanya, Chen, Zengzhao, He, Xiuling
• Format: Article
• Language: English
• Subjects: Redundancy; Semantics; Taxonomy
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551520952340

Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts.