## 2 Related Works  
### Knowledge Extraction  
The key step in constructing a KG is knowledge extraction. Knowledge extraction consists of two parts: Named Entity Recognition (NER) and Relation Extraction (RE).  
Currently, there are two main types of knowledge extraction tasks. One is the pipeline model, which first performs NER and then RE, such as the PL marker [8], proposed by [PERSON] et al. These approaches are flawed because they do not consider the intimate connection between NER and RE. Also, error propagation is another drawback of pipeline methods.  
In order to conquer these issues, the joint extraction of entity and relation is proposed, and demonstrates stronger performance on both tasks. HGERE [9], proposed by [PERSON] et al.,is a joint entity and relation extraction model equipped with a span pruning mechanism and a higher-order interaction module. It extracts features of entities and relations within a hypergraph. However, with a stronger encoder, performance improvement is not significant, and there is an issue with high computational efficiency. RFBFN [10], proposed by [PERSON] et al., extracts entities and relations simultaneously based on a Transformer. PRGC [11], proposed by [PERSON] et al., performs a joint extraction of entities and relations by aligning the head and tail entities identified through relation recognition. UIE [12], proposed by [PERSON] et al., uses prompt-based fine-tuning of Large Language Models (LLMs) to unify four types of natural language Information Extraction (IE) tasks, including entity extraction and relation extraction. [PERSON] et al. proposed the DA4 JIE [13] framework, which jointly solved four IE tasks in Unsupervised Domain Adaptation (UDA) setting. They employed an instance-relational domain adaptation method and incorporated context-invariant graph learning. However, a method that explicitly learns to find the optimal connections for the relation graph might be able to produce better performance for their problem.  
[PERSON] et al. proposed an information extraction model [14] that learned cross-instance dependencies through different layers of a Pre-trained Language Models (PLM) and cross-type dependencies via the Chow-Liu algorithm. However, this method does not explore other graph kernels to compute graph similarity. Moreover, recent structure learning methods are not evaluated. [PERSON] et al. formulated the real-world Attribute Extraction (AE) task into a unified Attribute Tree [15] and proposed a simple but effective tree-generation model to extract both in-schema and schema-free attributes from texts. However, one of the datasets used in their work, whose size is still small, produces randomness bias during the model training and may affect the final experimental results. They also did not conduct experiments on LLMs. [PERSON] et al. proposed a table-to-graph generation model [16], which unified the tasks of coreference resolution and relation extraction with a table filling framework, and leveraged a coarse-to-fine strategy to facilitate information sharing among these subtasks. However, experiments of this model are only conducted on domain-general documents. Additionally, it takes relatively more time and computational resources than models that only extract relations.