Abstract：Existing open information extraction methods in the attribute knowledge base extension heavily rely on deep syntax analysis or effective dictionary rules, thus the poor results in short text processing and low recall rates are produced. Therefore, an iterative bootstrapping attribute knowledge base extension algorithm based on word co-occurrence graph is proposed. The co-occurrence relationship between attribute and attribute values is employed to extend the knowledge base and a graph-based community discovery algorithm is designed to find out core nodes of the community. Finally, a model based on convolutional neural network is constructed to denoise the extraction results. Experiments on two real datasets show that the proposed method outperforms the existing ones.
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