## 1 Introduction  
To automatically expand new knowledge, obtaining new structured knowledge from massive amounts of unstructured data has become a popular research issue. Knowledge extraction technology represented by entity relation extraction has been successful. In recent years, in particular, supervised learning models have greatly promoted the development of specific relation triples extraction. However, compared with the complex challenges of extracting open relation triples in actual scenarios, some limitations remain with the existing methods. It is thus necessary to develop effective methods to resolve the problems of open domain triples extraction arising from actual scenario requirements.  
Traditional entity-relation triples extraction methods usually have a pre-defined closed relation set and, in previous research, tasks have been converted into a relation classification problem with good results. However, under the open relation triples extraction scenario, the text contains a large number of open entity relations, which far exceeds the number of pre-defined relation types. In this case, the traditional relation classification models cannot directly and effectively obtain the new type of relations between entities in the text. Determining a means to automatically discover new relations between entity pairs and implement open relation triples extraction remains a challenge.  
To achieve the extraction of open relation triples, some scholars have proposed the task of open relation extraction (ORE), which is dedicated to extract the relation facts between entities from unstructured text. Recently, [PERSON] [1] proposed a supervised open relation triples extraction framework that implements the free switching of supervised and weakly supervised modes through the relation siamese network (RSN), which can simultaneously use supervised data and unsupervised data of new relations in unstructured text to jointly learn the semantic similarity of different relation facts. Specifically, the RSN uses a siamese network structure to learn the deep semantic features of the relation samples and the semantic similarity between them from the labeled data of the pre-defined relation, which can be used to calculate the semantic similarity of the text containing the open relation. However, [PERSON] [2] proposed that for a new type of open relation, a few precise examples are needed as seeds, and a pre-trained relation siamese network method can be used to train an extraction model suitable for the new type of relation.  
This paper proposes an integrated open domain Chinese triples hierarchical extraction method to combine the advantages of deep learning with unsupervised algorithms and effectively expand the generalization ability of the open relation triples extraction model. The main contributions of this paper are as follows:  
* We propose an integrated open domain Chinese triples hierarchical extraction method with BERT-based deep learning and unsupervised learning algorithms.
* Based on the segmentation strategy of dynamically adding a user-defined entities dictionary, we implement a simple and efficient open relation triples automatic extraction algorithm based on attention mechanism.
* A large-scale Chinese named entity recognition dataset is constructed, which provides a support for carrying out Chinese named entity recognition tasks.