In past years, the development of knowledge graphs tailored to specific vertical domains has demonstrated significant application potential and economic worth. Although companies can easily utilize tools to dump structured data directly into the knowledge base, most of the knowledge still exists in unstructured text. Improving the automatic extraction efficiency of entities and relations has emerged as the key to fixing the problem [1]. Entity relation extraction, the central goal in the domain of information extraction, tries to extract set types of entities and relations of unorganized text and construct a structured knowledge triplet (main entity, relation, and guest entity), which is of great significance for downstream applications such as knowledge graph construction [2,3], intelligent question answering [4], and public opinion analysis [5]. Researchers typically categorize the problem into two main subtasks: named entity recognition [6–8] and relation extraction [9,10]. The two dominant research frameworks are the pipeline approach and the joint method.

As an early modeling method, the pipeline method trains two models to extract entities and classify relations, respectively. The entity model and the relation model can be trained using independent data sets, and it is not necessary to label them at the same time. According to the different execution orders of the model in the framework, it can be divided into the traditional entity relation model and the relation-entity model. However, because the two models are trained independently, and the post model is dependent upon the earlier model’s prediction outcomes, this leads to the problems of error accumulation and lack of interaction in this method [11]. In addition, the traditional pipeline method also has some entity redundancy [12].

Considering the aforementioned issues with the pipeline method, the end-to-end joint method as a novel modeling method has received a lot of attention [13,14]. According to the number of framework stages, the joint method can be divided into multi-task learning (two-stage) and structured prediction (single-stage) [15].

Multi-task learning. Although this approach adopts an end-to-end training framework, it inherently involves cascading tasks, making it fundamentally similar to the pipeline method. Specifically, it still constructs separate models for entity recognition and relation extraction, but with a key distinction: both tasks share a unified encoder and leverage parameter sharing for joint optimization. For example, reference [16] employed Conditional Random Fields (CRF) to reframe entity recognition and relation extraction as a multi-head selection task, effectively addressing multi-relation challenges. In a related effort, reference [17] proposed the CASREL framework, which utilizes a cascading binary markup strategy to model relations as functions mapping subjects to objects within sentences, thereby resolving the issue of overlapping relation triples. Similarly, reference [18] introduced SPERT, a span-based joint entity and relation extraction model utilizing an attention mechanism. This model mitigates the problem of entity overlap by incorporating advanced entity recognition and filtering mechanisms. Further innovations include the work of [19], which approached joint entity and relation extraction as a direct set prediction problem. This strategy eliminates the complexity of predicting the sequence of multiple triples, streamlining the extraction process. To tackle entity overlap more effectively, reference [20] proposed an end-to-end learning model integrated with a replication mechanism, enabling the simultaneous extraction of related facts from sentences of varying complexity. Moreover, a novel reading comprehension-based paradigm has gained prominence, reframing entity and relation extraction as a multi-turn question-answering task. This method transforms the extraction process into one of identifying the answer’s scope within the given context, offering a flexible and intuitive framework for handling intricate extraction scenarios [21]. However, similar to traditional pipeline methods, multi-task learning methods still have significant gaps in training and inference processes. This method has inherent leakage bias [22], and there is still an error accumulation problem in inference, thereby affecting the accuracy and reliability of the final results.

Structured prediction. Reference [23] introduced a relation-specific attention network (RSAN) that employs a relation-aware attention mechanism to generate tailored phrase representations for different relations. This model utilizes sequence labeling to identify and extract corresponding head and tail elements. Similarly, reference [24] proposed an innovative labeling scheme that reformulates the joint extraction task as a labeling problem, enabling the direct extraction of entities and their relations in a unified manner. Additionally, reference [22] presented a single-stage joint extraction model, TPLinker, which redefines the task as a token pair linking problem, effectively addressing the issue of exposure bias in the extraction process.

While the structured prediction method successfully mitigates issues such as exposure bias, entity overlap, and missing interactions, it does not necessarily imply that the joint method outperforms the pipeline method [15]. In fact, current methods for entity and relation extraction have some limitations, such as difficulty in handling nested entity and relation overlap, which pose challenges for models in extracting complex knowledge. In addition, when the encoding dimension of the data is very high, the model may encounter difficulties in training due to extremely sparse data. Consequently, the development of novel syntax-enhanced models is essential to effectively overcome these limitations and enhance the accuracy and robustness of entity relation extraction.

This paper adopts the pipeline extraction method of relation before entity, based on the Chinese pre-trained BERT [25] (i.e., Bidirectional Encoder Representation from Transformers) model called “BERT-wwm-ext” [26]. Two independently trained models are included in the pipeline extraction method, which are called entity model and relation model. The predicted value of the relation model is supplemented by the input of the entity model as an additional semantic feature and combined with the “Queue” input scheme proposed in this paper. The model effectively solves the problems of relation overlap and entity overlap that were neglected by previous research work. In addition, it benefits from the excellent performance of the pre-trained model BERT in the field of text classification (96%), we combine BERT with POS information to further enhance the performance of the relation model. On the basis of alleviating the exposure bias of the pipeline method as much as possible, it effectively avoids the limitation of the joint extraction model in processing complex samples.

In this paper, a comprehensive comparative experiment is carried out on the self-constructed data set EPI. The experimental results show that the performance fluctuation of subtasks caused by error accumulation decreased by 3.21%, while the entity relation triplet extraction performance increased by 1.91%, which proves the effectiveness of the model.

The main contributions of this paper are as follows:

(1) This paper proposes CN-POS and introduces the POS information represented by it in the pre-trained language model, which solves the problem that Chinese POS and character-level encoding cannot be aligned, and enhances the model’s semantic understanding ability of syntactic information.

(2) This paper introduces the RF framework, which employs a “Queue” scheme to link the relation model and entity model. This approach directs the model’s attention to specific parts of the input sentence corresponding to different relations, thereby strengthening the semantic interaction between relations and entities and mitigating the effects of knowledge overlap.

(3) This paper constructs an EPI dataset and proposes SE-RE, achieving optimal performance on the dataset.

The remainder of this paper is structured as follows: Section 2 presents the dataset and task description. Section 3 details the proposed model and algorithm. Section 4 outlines the experimental setup, followed by results and discussion in Section 5. Finally, Section 6 concludes the paper.

The starting point of this paper is to provide a high-performance model of automatic extraction of entity relation for the vertical domain knowledge graph based on the construction of the ontology layer [27]. However, most of the existing public datasets are oriented to the public domain and rarely involve professional fields. Therefore, they are mostly simple sentences with low semantic complexity, which are reflected in the following characteristics: (1) short sentences (2) each sentence contains only one or two knowledge triples (3) the semantic differences of the entities in sentences are obvious and easy to recognize (e.g., person, location, organization, time, etc.), and knowledge extraction can be achieved through some excellent NLP tools.

A dataset called EPI describing e-commerce listings was constructed in this paper, which is a complex sample with high-density knowledge in the e-commerce field. The data for the EPI dataset is obtained from commonly used e-commerce platforms without additional processing or reduction, preserving the integrity of the original data. This characteristic ensures its applicability in a wide range of scenarios, accurately reflecting the features and complexities of real-world e-commerce data. We extracted information about 1653 products and obtained 5498 sample data through sentence segmentation. Each sample describes the information on related products. A total of 14 relation types and 12 entity categories are defined, which constitute 14 schemas. The goal is to extract structured knowledge triple RDF (Resource Description Framework). A specific example is shown in Table 1.

It is worth noting that: (1) A product can have multiple aliases, be sold on multiple platforms, and be suitable for a variety of scenarios (2) Different styles and colors of a product will interfere with the price (3) Different models of a product have differences in function, material and price (4) The promotional price of the product in different time periods is different (5) Because Chinese places a strong focus on semantics and less on framework, there are a large number of reference omissions and irregular splicing vocabulary in the samples.

Considering the limitations of existing entity relation extraction methods in effectively modeling the interactions between entities and relations, this paper proposes SE-RE, a novel approach designed to tackle the challenges of entity nesting and relation overlap in complex semantic texts. The overall structure of the model is illustrated in Fig. 1, while Fig. 2 provides a detailed case study. The model architecture comprises the following three main components: (1) To enhance the semantic modeling ability of each extraction model for Chinese syntactic information, this paper proposes CN-POS and introduces it into the Chinese pre-trained BERT model to compensate for its shortcomings in POS feature modeling. (2) To improve the interaction between entity recognition and relation extraction models, this paper proposes a “Queue” input scheme and RF to achieve syntactic and semantic enhancement based on different relations. (3) To alleviate the problems of relation overlap and entity redundancy, this paper employs a “Queue” input framework to feed the strengthened input sequences into the entity recognition model in order to obtain entity pairs cascaded by the current relation.

Dataset. This paper verifies the proposed SE-RE model on the self-constructed EPI data set, which is oriented to complex samples with high-density knowledge in the e-commerce domain. The dataset is divided into training, development, and testing sets using random sampling while ensuring that the sample distributions accurately represent the characteristics of the original dataset. This approach maintains consistency in data patterns across all subsets, enabling robust model training and evaluation (See Section 2 for a detailed introduction of the dataset). Table 2 presents the sample distribution for the relation model, illustrating the frequency and proportions of various relation types within the dataset. Similarly, Table 3 details the sample distribution for the entity model (i.e., the single relation sample obtained by the RF mechanism), showcasing the distribution of different entity categories. These tables provide a comprehensive overview of the dataset’s composition for both models, ensuring transparency in the data preparation process. The overall sample distribution of each relation can be observed in Table 4.

Parameter setting. During the training phase, the model employs an Adam optimizer with a linear scheduler for parameter adjustment, incorporating a warmup ratio of 0.1. The training process spans 10 epochs with a dropout rate of 0.1 (keep ratio of 0.9) and a batch size of 32. The learning rate is set at 5e − 5, the maximum sequence length is limited to 128, and the hidden layer dimension of the Bi-LSTM network is 256. Notably, the output of the relation extraction model plays a pivotal role in determining the overall performance of the SE-RE model. Therefore, in order to improve the performance of the relation extraction model as much as possible, this paper incorporates an F1-score threshold into the training of each epoch. Finally, when the threshold is 0.45, the F1-score reaches the optimal value.

Baselines. This article conducts comparative experiments between the SE-RE model proposed for the entity relation extraction task and the most representative state-of-the-art models on the EPI dataset. The detailed introduction of the baseline models is as follows:

Multi-head [16]: As a standard method for joint entity relation extraction, Multi-head adopts a span-based extraction framework that can simultaneously perform entity recognition and relation extraction without any manual input or additional tools, effectively addressing the situation in which the same entity connects to multiple relations.

Pipeline [15]: The entity recognition stage is carried out before the relation extraction step in this traditional pipelined approach to entity-relation extraction, which splits the operation into two separate subtasks. Special entity boundary labels are then constructed from the output of the entity model to model the dependency relation between entity pairs.

SpERT.PL [32]: This method further enriches the context embedding of pre-trained converters by embedding POS tags. Specifically, The method assigns the POS of the entity to each token that constitutes the entity and incorporates the POS into the Transformer-based pre-trained language model. Additionally, the method introduces a large number of additional features, such as the span range feature within the entity, the overall span feature, the correlation feature between entities, and the correlation feature between entity categories and relations.

SE-RE(PL): To further demonstrate the effectiveness of the proposed CN-POS, this paper replaces the POS feature encoding strategy in the SE-RE model with the direct splicing method used in the SpERT.PL model.

SE-RE(MRC): To further demonstrate the effectiveness of the RF mechanism proposed in this paper, the relation syntax-enhanced strategy in the SE-RE model is replaced with the contextual sentence format used in the BERT-MRC model, namely “text_b: original text sequence”.

SE-RE(-POS): To further validate the effectiveness of incorporating POS information into the entity relation extraction task and highlight the advancements achieved by the CN-POS enhanced pre-trained encoding model (i.e., the improved version of the traditional BERT-wwm-ext model) proposed in this paper, detailed experiments and evaluations were conducted. We remove all POS information from the SE-RE model for comparative analysis.

Evaluation. In this paper, we use the standard evaluation metrics for the entity relation extraction task: Precision (P), Recall (R), and F1-score (F1) to evaluate the relation extraction model, the entity recognition model, and the final ternary cascade results. It is worth noting that some baseline models adopt a span-based modeling strategy. Therefore, in the entity recognition task, this paper adopts a strict evaluation principle, which means that the output entity span results need to be completely consistent with the true values in order to be considered accurate prediction results.

In this section, we analyze the experimental results from three key perspectives: the entity-relation extraction framework, the entity-relation model feature interaction strategy, and the ablation experiment.

This paper proposes SE-RE to address entity nesting and relation overlap in complex semantic texts. The aim is to extract structured entity relation triplets from text descriptions in the unstructured e-commerce product information domain. Taking inspiration from previous research that used POS information to enhance model performance, this paper constructs CN-POS and introduces it into the Chinese pre-trained BERT model, the problem of POS noise and insufficient feature training introduced by the traditional feature splicing approach is effectively alleviated. Inspired by the semantic core of predicates in Chinese, this paper adopts a model framework of relation before entity, effectively alleviating the problem of error accumulation. On this basis, the use of the “Queue” scheme and RF mechanism enhances the interaction between relation and entity, greatly improving the cascading errors caused by relation overlap. Additionally, this strategy can guide the entity model to obtain more accurate boundaries of nested entities.