[1]黄廷磊,张伟莉,梁霄,等.数据驱动的细粒度中文属性对齐方法[J].东南大学学报(自然科学版),2017,47(4):660-666.[doi:10.3969/j.issn.1001-0505.2017.04.006]
　Huang Tinglei,Zhang Weili,Liang Xiao,et al.Data-driven method for fine-grained property alignment between Chinese open datasets[J].Journal of Southeast University (Natural Science Edition),2017,47(4):660-666.[doi:10.3969/j.issn.1001-0505.2017.04.006]
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数据驱动的细粒度中文属性对齐方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第4期
页码:
660-666
栏目:
自动化
出版日期:
2017-07-20

文章信息/Info

Title:
Data-driven method for fine-grained property alignment between Chinese open datasets
作者:
黄廷磊1; 2; 张伟莉1; 2; 3; 梁霄1; 2; 付琨1; 2
1中科院空间信息处理与应用系统技术重点实验室, 北京 100190; 2中国科学院电子学研究所, 北京 100190; 3中国科学院大学, 北京 100049
Author(s):
Huang Tinglei1; 2;  Zhang Weili1; 2; 3;  Liang Xiao1; 2;  Fu Kun1; 2
1CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China
2 Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
关键词:
中文属性对齐;  属性数据类型判别;  属性相似度;  异构数据集成;  知识图谱构建
Keywords:
Chinese property alignment;  property data type determination;  similarity of properties;  heterogeneous data integration;  construction of knowledge graphs
分类号:
TP182
DOI:
10.3969/j.issn.1001-0505.2017.04.006
摘要:
为提高中文开源数据集间属性关系识别的准确率,提出一种数据驱动的细粒度对齐方法,综合利用属性的扩展、定义域等对属性间的同义、包含、相关等关系进行统一识别.方法首先利用统计理论确定属性的数据类型,并给出类型感知的属性相似度计算方法.在此基础上,将属性关系识别建模为多分类问题,抽取有效特征对不同关系进行描述并用于随机森林模型的构建.实验结果表明,该方法中属性数据类型判别的准确率达94.6%,最终对同义、包含、相关关系识别的F1值分别为71.3%,57.3%及59.9%.相比只关注同义属性的传统方法,细粒度属性对齐方法不仅提高了同义属性识别的准确性,而且可识别出相互包含和相关的属性,证明了其在中文开源数据集上的有效性.
Abstract:
In order to improve the performance of property alignment between heterogeneous Chinese open datasets, a data-driven method for fine-grained alignment is proposed, which exploits the extension and domain information of properties to find equivalence, subsumption and relevance relations between properties in a unified way. First, the data types of properties are determined utilizing statistical theory, and a type-aware metric is given to calculate the similarity of properties. Based on that, the property relation recognition is modeled as a multi-classification problem, and effective features are generated to represent different property relationships and construct the random forest classifier. The experimental results show that, the proposed method can reach a precision of 94.6% in determining data types of properties, and the final F1 measures in recognizing equivalent, subsumptive and relevant properties are 71.3%, 57.3% and 59.9%, respectively. Compared with the traditional approaches that only focus on equivalent properties, the fine-grained property alignment method can improve the precision in recognizing equivalent properties, and recognize subsumptive and relevant properties, proving its effectiveness on Chinese open datasets.

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备注/Memo

备注/Memo:
收稿日期: 2016-11-15.
作者简介: 黄廷磊(1971—),男,博士,教授,博士生导师,tlhuang@mail.ie.ac.cn.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2012AA011005).
引用本文: 黄廷磊,张伟莉,梁霄,等.数据驱动的细粒度中文属性对齐方法[J].东南大学学报(自然科学版),2017,47(4):660-666. DOI:10.3969/j.issn.1001-0505.2017.04.006.
更新日期/Last Update: 2017-07-20