基于无向分块加权图的无模式实体识别方法研究.

The blocking approach ignored the weight of block keys and ambiguity between blocking keys,leading to low accuracy. This paper proposed a schema-agnostic entity resolution method based on undirected weighted graph. The method extracted attributes from data sources,combined attribute information entropy and TF-IDF to get clustering attributes and established an unified block scheme. Through the relationship between clustering attribute weight and block key, it gave each group of block key a certain weight. The weight and the cooccurrence frequency of the edge were multiplied and weighted to form the undirected block weighted graph. Finally, it used the pruning scheme to prune the edges. The problem of multi-attribute and block key ambiguity in data was solved and increased accuracy. Experiments on seven real data sets show that the method is effective and scalable. [ABSTRACT FROM AUTHOR]