TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data

Ting WANG  Tiansheng XU  Zheng TANG  Yuki TODO  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D    No.6    pp.1251-1261
Publication Date: 2017/06/01
Publicized: 2017/03/15
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7307
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Semantic Web,  linked open data,  ontology mapping,  similarity computing,  TongYiCiCiLin,  

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Linked Open Data (LOD) at Schema-Level and knowledge described in Chinese is an important part of the LOD project. Previous work generally ignored the rules of word-order sensitivity and polysemy in Chinese or could not deal with the out-of-vocabulary (OOV) mapping task. There is still no efficient system for large-scale Chinese ontology mapping. In order to solve the problem, this study proposes a novel TongYiCiCiLin (TYCCL) and Sequence Alignment-based Chinese Ontology Mapping model, which is called TongSACOM, to evaluate Chinese concept similarity in LOD environment. Firstly, an improved TYCCL-based similarity algorithm is proposed to compute the similarity between atomic Chinese concepts that have been included in TYCCL. Secondly, a global sequence-alignment and improved TYCCL-based combined algorithm is proposed to evaluate the similarity between Chinese OOV. Finally, comparing the TongSACOM to other typical similarity computing algorithms, and the results prove that it has higher overall performance and usability. This study may have important practical significance for promoting Chinese knowledge sharing, reusing, interoperation and it can be widely applied in the related area of Chinese information processing.