## 1 Introduction  
Relational extraction tasks involve the extraction of relational facts from unstructured or semi-structured data to identify interactions and attributes between entities [1]. The construction of a knowledge graph typically involves the extraction of information from unstructured information, which is also known as entity-relationship extraction. In relationship extraction, the effective characterization of domain knowledge is challenging [2, 3]. Due to the intricate and complex nature of the expertise involved in recording relevant data within the power system [4, 5], the effective identification of relationships in this specialized domain is challenging. A fundamental knowledge graph can be established by first conducting named entity recognition on unstructured data within a specialized domain and subsequently extracting relationships from the identified entities. In the deep learning field, named entity recognition and relation extraction are important tasks in natural language processing [6]. The relationships between different entities were extracted using a rule-based approach. [PERSON] [7] proposed a bootstrap labeling rule discovery approach for robust relation extraction. However, this rule-based approach had low accuracy and was unable to characterize the semantics of lexical elements through vectors via deep learning approaches. [PERSON] [8] proposed a RoFormerV-BiLSTM-CRF based fusion model for medical entity recognition, which used a knowledge graph to analyze the relationships between the medical entities identified in single and multiple patient medical records. [PERSON] [9] proposed a framework for the automatic construction of a process knowledge base in the processing domain based on a knowledge graph. He also developed a knowledge extraction framework that employed BERT-BiLSTM-CRF for the automatic retrieval of knowledge from the process text. [PERSON] [10] proposed a span-based multimodal attention network (SMAN) for joint entity and relation extraction, and introduced a completion mechanism to simultaneously extract the context and span position information. [PERSON] [11] proposed a new pipelined relationship extraction framework that utilized an attentional mechanism to fuse contextual semantic representations, which was able to capture entity location information and type information that are challenging to incorporate into joint models. The extraction of entity relationships through deep learning is mainly categorized into two methods. The first method is the joint extraction model, where entity recognition and relationship extraction are treated as a whole [12; 13]. The second method is the pipeline model, in which entity recognition and relationship extraction are considered as distinct tasks that are handled independently [14; 15; 16]. The pipeline approach does not require manual feature construction, which makes it more widely used. The entity recognition task focuses on the identification of real words in the text, while the relationship extraction task tends to the modeling of the links between entities; the separate recognition of entities and relationships enables targeted improvement of the two tasks. The joint extraction can consider the two entities and relations, which avoids the negative impact of errors generated through the entity recognition of the pipelined model on the subsequent task of relation extraction. However, recent studies on the pipelined model led to the improvement of the error propagation problem, which resulted in enhanced recognition through the joint extraction model. [PERSON] [17] sliced the English vocabulary into its roots and utilized span annotation. The enumerated candidate entities were spliced with the sentences as a training example. This approach effectively enhanced the accuracy of the downstream task of relationship extraction.