Topological Features Based Entity Disambiguation  

作　　者：Chen-Chen Sun De-Rong Shen Yue Kou Tie-Zheng Nie Ge Yu 

机构地区：[1]School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

出　　处：《Journal of Computer Science & Technology》2016年第5期1053-1068,共16页计算机科学技术学报（英文版）

基　　金：This work is supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the Fundamental Research Funds for the Central Universities of China under Grant No. N120816001, and the National Natural Science Foundation of China under Grant Nos. 61472070 and 61402213.

摘　　要：This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.

关 键 词：entity disambiguation topological feature CLUSTERING random walk with restarts 

分 类 号：TP391.2[自动化与计算机技术—计算机应用技术] Q516[自动化与计算机技术—计算机科学与技术]