Extracting Attributes and Synonymous Attributes from Online Encyclopedias

In this paper, we present an approach that extracts attributes of open-domain named entities for the Chinese language. The approach contains two steps. The first step consists in an unsupervised technique which captures high frequency attributes from online encyclopedias. The second step discovers uncommon attributes with low frequency. Lastly, an integrated framework is proposed to obtain attributes and their synonymous attributes simultaneously. Experimental results show that the proposed approach boosts the coverage of extracted attributes without losing the precision.