基于交通领域知识网络的词汇语义相似度计算  

Measuring Semantic Similarity of Words Based on Traffic Field Knowledge Network

作　　者：黄浩[1] 陈怀新[1] HUANG Hao CHEN Huai-Xin(China Electronics Technology Group Corporation No. 10 Research Institute, Chengdu 610036, China)

机构地区：[1]中国电子科技集团第十研究所,成都610036

出　　处：《计算机系统应用》2017年第3期169-174,共6页Computer Systems & Applications

摘　　要：针对传统基于wordnet的词汇语义相似度计算方法中隔离抽象词汇和具象词汇,以及片面依赖上下义关系的不足,提出了基于交通领域知识网络的词汇语义相似度计算方法.基于上下义、工具-工具对象、部件-整体等概念关系准则构建了交通词汇的知识网络图谱,提出了修正的平均路径长度参量计算网络中词汇的语义相似度,得到更高的语义一致性结果.实验表明,在Finkelstein的353对词汇集上,本文算法能够获得比传统方法更符合人工判断的语义相似度.The traditional way of calculating word semantic similarity is based on wordnet structure, which has a huge gap between physical concept and abstract concept, and only considering concepts＇ hyponymy. To solve the problem, a novel word similarity calculation algorithm based on traffic field words relation network is proposed in the paper. 10 kinds of concept relationships, including concepts of hyponymy, tool-tool object relationship, standard parts-overall and so on, are used to build traffic words knowledge network. Then modified average path length parameter is used to calculate words＇ semantic similarity, which accords with people＇s judgement. The experiment based on Finkelstein＇s 353 word pairs shows that the algorithm achieves more accurate word semantic similarity.

关 键 词：词汇语义相似度 领域知识网络 平均路径长度 WORDNET 概念关系准则 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]