Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion

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Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion

更新时间：2025-07-09

- Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion
- Vol. 44, Issue 2, Pages: 20-24(2010)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Online First：10 February 2010，
  
  Published：2010
- 稿件说明：
移动端阅览
Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion[J]. 2010, 44(2): 20-24.
DOI：

Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion[J]. 2010, 44(2): 20-24. DOI：

摘要

针对基于元数据或传统主题图的知识组织模式没有实现知识的多层次多粒度表示

以及知识融合过程中相似性算法准确性不高而影响融合质量的问题

结合全信息理论与扩展主题图结构特点及语义信息

提出了面向多源知识融合的扩展主题图相似性算法(ETMSC)和阈值选取的相关性、层次对应和实验确定三原则. 该算法综合了语法、语义和语用的相似性

扩展了主题图元素间组成结构上的相似性

同时充分考虑了涵义及所处语境的相似性.主题图相似性的判别准则与阈值有关

阈值的确定与数据集相关. 实验结果表明

ETMSC算法与单纯基于语法或语义的相似性算法相比

准确性提高了9.2%～11.1%.

Abstract

A novel similarity algorithm of extended topic map called ETMSC for multi-resource knowledge fusion is proposed to improve the drawbacks that the knowledge organization model based on metadata or traditional topic map can not represent knowledge multi-level and multi-granularity

and the low accuracy of existing similarity algorithms. Three principles of the correlation

levels corresponding

and the experimental determination in selecting threshold are presented. The algorithm combines the comprehensive information theory with the structure and semantic information of extended topic map. The syntactic matching

semantic matching

and pragmatic matching are comprehensively considered

in which not only the structural similarity of topic map elements are extended

but also the meaning and relevance in linguistic contexts are thoroughly taken into account. Topic map similarity criterions are related to a threshold

and the determination of the threshold is associated with the data sets. Experimental results and comparisons with the traditional algorithms that are purely based on the syntactic or semantic similarity show that the F-measure of ETMSC is improved by 9.2%-11.1%.

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references

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