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

Ting WANG; Tiansheng XU; Zheng TANG; Yuki TODO

doi:10.1587/transinf.2016EDP7307

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

Ting WANG, Tiansheng XU, Zheng TANG, Yuki TODO

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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.

Publication: IEICE TRANSACTIONS on Information 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

Authors

Ting WANG
  Capital University of Economics and Business
Tiansheng XU
  Capital University of Economics and Business
Zheng TANG
  University of Toyama
Yuki TODO
  Kanazawa University

Keyword

Semantic Web, linked open data, ontology mapping, similarity computing, TongYiCiCiLin

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Ting WANG, Tiansheng XU, Zheng TANG, Yuki TODO, "TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 6, pp. 1251-1261, June 2017, doi: 10.1587/transinf.2016EDP7307.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7307/_p

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@ARTICLE{e100-d_6_1251,
author={Ting WANG, Tiansheng XU, Zheng TANG, Yuki TODO, },
journal={IEICE TRANSACTIONS on Information},
title={TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data},
year={2017},
volume={E100-D},
number={6},
pages={1251-1261},
abstract={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.},
keywords={},
doi={10.1587/transinf.2016EDP7307},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data
T2 - IEICE TRANSACTIONS on Information
SP - 1251
EP - 1261
AU - Ting WANG
AU - Tiansheng XU
AU - Zheng TANG
AU - Yuki TODO
PY - 2017
DO - 10.1587/transinf.2016EDP7307
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2017
AB - 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.
ER -