The relationship triplet is regarded as a crucial component in knowledge graphs, typically represented in the structure (h, r, t), where h denotes the head entity, t signifies the tail entity, and r describes the relationship between the two entities. For instance, the triplet (Paris, capital_of, France) conveys that “Paris is the capital of France.” To construct knowledge graphs effectively, the extraction of relationship triplets is identified as a vital step, which involves the task of extracting entities and their interrelations from unstructured natural language text. This extraction process is generally divided into two key steps. The first step is named entity recognition [1,2], which involves the identification of all specific entities within the text, including names of people, locations, and organizations. The accuracy of this step is critical, as it relies on the ability to extract entities from diverse and complex text data, providing the foundational elements necessary for subsequent relationship extraction. Following this, the second step is relationship extraction [3,4], wherein the relationships existing between the identified entities are analyzed and categorized. The challenge inherent in this phase is the accurate identification of the semantics of relationships, particularly when relationships are implied or expressed in various forms. Consequently, by integrating the recognized entities with their corresponding relationships, a complete triplet is constructed, which is further utilized for the expansion and refinement of the knowledge graph.

This comprehensive process not only enhances the quality of structured information within knowledge graphs but also establishes a solid foundation for automated information inference and knowledge discovery. Overall, relationship triplet extraction has consistently been a prominent research topic in the field of natural language processing. Fig. 1 illustrates the timeline of the development of the relation triplet extraction task. In earlier studies, pipeline models [5] were predominantly employed to accomplish the triplet extraction task. Within this model framework, named entity recognition and relationship extraction were systematically decomposed into two independent subtasks. Initially, in the named entity recognition phase, potential entity pairs were identified from the text; subsequently, in the relationship extraction phase, attempts were made to determine and classify the relationships between the recognized entity pairs. Although this sequential processing approach simplifies the complexity of the task, it simultaneously introduces several challenges and limitations, such as the following:

1. Error Propagation: The main flaw of the pipeline model lies in the information loss resulting from the independence of the subtasks. The lack of effective context and interaction information transfer between named entity recognition and relationship extraction can lead to the accumulation of errors. For instance, if named entity recognition incorrectly labels an entity, this error will prevent the relationship extraction phase from accurately identifying or classifying the relationship between that entity and others. As the tasks are processed sequentially, errors gradually amplify, negatively impacting the effectiveness of the final relationship triplet extraction.

2. Lack of Interaction: Because the two subtasks work independently, the model cannot share information or leverage the potential synergies between entity recognition and relationship classification. For instance, the existence of certain relationships could provide valuable clues for identifying specific entity types, but this information cannot be effectively utilized due to the independent nature of the tasks.

3. Information Loss: In each independent subtask, some useful contextual information might be ignored or lost. This loss of information across tasks can negatively impact the overall extraction performance.

4. Independent Optimization: The pipeline model usually optimizes each subtask independently, meaning that each step’s model is trained and optimized separately without considering the global optimal solution. This independent optimization may result in a decrease in the model’s overall performance.

5. Limited Ability to Handle Complex Relationships: Since each step can only handle local information, it is difficult to capture the global syntactic and semantic structure. As a result, entities and relationships in complex sentences may not be accurately extracted or parsed.

6. High Time and Computational Costs: Each independent subtask requires separate model training and execution, leading to higher time and computational costs for the pipeline model, making it relatively less efficient overall.

In recent years, with a deepening understanding of the limitations of the pipeline model [6], joint modeling of entity recognition and relationship extraction has gradually become a focal point of research. This strategy of joint modeling aims to achieve the extraction of relationship triplets through a single model framework, thereby enhancing overall processing efficiency and accuracy while reducing errors in information transfer. The joint extraction model emerges as a transformative approach, markedly enhancing extraction accuracy and efficiency through the simultaneous recognition of entities and extraction of relationships. This method, while adeptly minimizing computational resource consumption, facilitates a more nuanced understanding of contextual information. By fostering synergistic effects between tasks, it enables the model to exhibit greater flexibility and precision throughout the information extraction process.

In particular, the ability to share information across multiple tasks plays a crucial role in reducing error rates during extraction [7]. Furthermore, the incorporation of rich feature representations not only amplifies the model’s generalization capabilities but also allows it to adapt seamlessly to diverse data scenarios. Thus, the joint extraction strategy not only streamlines the data processing workflow but also presents a compelling solution for applications in natural language processing and knowledge graph construction [8]. Ultimately, this approach underscores the profound insights and practical significance inherent in cutting-edge research, paving the way for future advancements in the field.

In summary, the contributions of this review can be articulated as follows:

1. The paper systematically categorizes joint extraction models for relationship triplets into two distinct types based on their architectural frameworks: joint decoding methods and parameter sharing methods. Additionally, it provides a succinct overview of commonly utilized datasets and evaluation criteria relevant to the task of relationship triplet extraction, thereby establishing a foundational understanding for researchers.

2. A thorough analysis and comparative study of relationship triplet extraction models that employ joint decoding methods is presented, which are further classified into three categories: table filling methods, tagging methods, and sequence-to-sequence methods. This classification not only highlights the diversity of approaches but also elucidates their respective strengths and applications.

3. Furthermore, the paper delves into a comparative analysis of models utilizing parameter sharing methods, engaging in a comprehensive discussion of related work. This exploration serves to illuminate the nuances and advancements in this area.

4. Finally, a brief yet insightful overview of the strengths and weaknesses of existing joint extraction models for relationship triplets is provided, alongside an exploration of their application domains. This culminates in a forward-looking perspective on future developments in the field, aiming to inspire further research and innovation.

Due to the possibility of relationship triplets sharing one or two entities, this overlap phenomenon complicates the extraction task. Based on the characteristics of entity overlap [9], sentences can be categorized into three types: (i) No Entity Overlap (NEO): This type of sentence contains one or more triplets, but there are no shared entities among them. (ii) Entity Pair Overlap (EPO): Sentences of this kind feature multiple triplets, where at least two triplets share the same entities, which may be in the same order or in reverse order. (iii) Single Entity Overlap (SEO): In this case, a sentence contains multiple triplets, with at least two triplets sharing one common entity. The primary objective of relationship triplet extraction is to identify all existing relationship triplets present in the sentence.

Traditional relationship triplet extraction techniques typically divide this task into two independent phases: first, entity recognition is performed, followed by the analysis of the relationships between these entities based on contextual relationships. However, employing a joint modeling approach allows for the simultaneous execution of entity recognition and relationship extraction, thereby forming an integrated model that simplifies this process. This joint modeling strategy enhances the synergy between entity recognition and relationship extraction by sharing contextual information. Consequently, this information-sharing mechanism significantly improves the accuracy and overall efficiency of the extraction process.

There are many datasets available for relational triplet extraction. For instance, WikiEvents offers a wealth of event descriptions along with their associated entities and relationships, making it particularly suitable for understanding event-driven relations. Meanwhile, TACRED serves as a comprehensive benchmark dataset that encompasses various types of texts and relationships, thus becoming a crucial reference for evaluating model performance. Additionally, the SemEval [10] series of competitions presents multilingual datasets that broaden the application scenarios for relation extraction. Lastly, the ACE [11] dataset is dedicated to extracting events and relationships from news articles and other texts, thereby enriching the research materials available in this domain. However, this paper will focus on detailing the two most widely used datasets in recent years: NYT and WebNLG.

In the process of relationship triplet extraction, it is crucial to accurately identify the boundaries and types of entities, as well as the relationship between the head entity and the tail entity. Only when these elements are correctly identified can the extracted relationship triplets be considered valid. For this task, several commonly used evaluation metrics [14] are as follows:

Precision (Prec.): This refers to the ratio of the number of correctly identified relationship triplets to the total number of triplets identified by the model. The calculation formula is as follows: (1)Precision=TPTP+FPwhere TP (true positive): denotes the identification of a completely correct relational triad; FP (false positive): denotes that it is not the relational triad but the model determines it to be a relational triad.

Recall (Rec.): This measures the proportion of relationship triplets successfully identified by the model among all the relationship triplets that actually exist. The calculation formula is as follows: (2)Recall=TPTP+FNwhere FN (false negative): indicates that it should have been recognized but the model did not actually recognize it.

F1 score (F1): This metric is the harmonic mean of precision and recall, providing a comprehensive assessment of performance in both aspects. A higher F1 score reflects the overall effectiveness of the model. (3)F1=2Precision×RecallPrecision+Recall

Micro-average: An overall evaluation of all relational triplets, calculating global Precision, Recall, and F1. This approach treats each instance equally and combines the contributions of all categories into a single set of metrics, giving equal weight to each instance. (4)Precisionmicro=∑i=1nTPi∑i=1nTPi+∑i=1nFPi (5)Recallmicro=∑i=1nTPi∑i=1nTPi+∑i=1nFNi (6)micro−F1=2×Precisionmicro×RecallmicroPrecisionmicro+Recallmicro

Macro-average: As an evaluation method, this computes the Precision, Recall, and F1 for each category separately, and then averages these results. In this process, all categories are treated equally, regardless of the number of samples in each category, ensuring that the performance of smaller categories receives appropriate attention and is not overshadowed by larger categories. (7)Precisionmacro=1n∑i=1nPrecisioni (8)Recallmacro=1n∑i=1nRecalli (9)macro−F1=2×Precisionmacro×RecallmacroPrecisionmacro+Recallmacro

Top-k Evaluation Metrics: When a system can output multiple candidate triplets, Top-k evaluation metrics such as Top-1, Top-5, Top-10, etc., are used to assess the model’s performance. Specifically, the essence of the Top-k metric lies in its ability to assess the proportion of correct results among the top k outcomes returned by a model. This metric enjoys widespread popularity due to its simplicity and ease of understanding; however, it is crucial to acknowledge its limitations. Notably, the Top-k metric focuses solely on the top k results, which may lead to the oversight of the overall quality of the results. This characteristic becomes particularly pronounced in the context of relationship triplet extraction tasks. Consequently, the Top-k metric is often best employed in conjunction with other evaluation metrics to achieve a more comprehensive assessment of model performance.

By integrating multiple evaluation dimensions, researchers are better positioned to gain a nuanced understanding of how models perform on complex tasks, thereby avoiding the pitfalls of relying on a singular metric that could yield a skewed perspective. Therefore, adopting a diversified set of evaluation standards in the assessment of relationship triplet extraction performance is instrumental in uncovering both the strengths and weaknesses of models, ultimately fostering advancement within this field.

In order to validate the reproducibility of current state-of-the-art relational triplet extraction techniques and address the lack of performance comparisons across different models under uniform conditions, this paper will conduct small-scale comparative experiments under consistent hardware settings. These experiments will utilize the same datasets and evaluation metrics to ensure fairness and reliability. Moreover, additional small-scale experiments will be carried out, focusing on the number of relational triplets present in sentences and the types of overlapping triplets. Through these analyses, the paper aims to evaluate and compare the performance of various extraction models across different datasets, providing a deeper understanding of their strengths and limitations in diverse scenarios.

Despite the significant advancements achieved in accuracy and efficiency by triplet joint extraction models, their limitations remain noteworthy. Primarily, the issue of computational complexity often restricts their practical application. Table 8 shows the computational complexity and applicable scenarios of different relation triplet extraction techniques; particularly when handling large-scale data, the training and inference processes of these models can consume substantial computational resources, resulting in extended response times. Additionally, concerns regarding scalability arise, as many existing methods struggle to maintain consistent performance in the face of an ever-increasing variety of entities and relation types. More critically, in resource-constrained environments, the performance of these models may decline markedly, given that their updates typically necessitate extensive annotated data and computational resources.

In addition, as the complexity of relation triplet extraction models continues to increase, the computational resources required for training these models, along with their environmental impact, have become increasingly critical concerns. The training of large-scale deep learning models typically relies on high-performance computing hardware, such as GPUs or TPUs, which consume substantial amounts of electricity and contribute significantly to carbon emissions. Particularly when dealing with vast amounts of textual data, the training process can extend over several days or even weeks, further escalating energy consumption and the environmental burden [76]. Moreover, in order to achieve high model performance, researchers often depend on large datasets and distributed computing frameworks, which not only place greater demands on computational power but also increase energy consumption associated with data storage and transmission. Given the growing importance of sustainability, finding ways to optimize the use of computational resources while reducing environmental impact, without compromising model performance, has emerged as a key research direction in the field.

In recent years, Transformer-based architectures such as BERT [77] and GPT-3 have demonstrated immense potential in the field of relational triplet extraction. BERT, with its bidirectional encoder and pretraining-finetuning framework, effectively captures contextual information, thereby enhancing the accuracy and generalization capability of relation extraction tasks. On the other hand, GPT-3 leverages its powerful generative capacity and few-shot learning advantage, allowing it to excel in complex and low-resource environments. Both models combine robust language modeling abilities and exhibit unique strengths when dealing with intricate sentence structures and multiple relations. Furthermore, the integration of technologies like Graph Neural Networks (GNN) can further enhance model performance, thereby positioning Transformer-based architectures as highly promising and valuable in the context of relational triplet extraction tasks, with significant potential for both application and research advancement.

On the application front, the potential of joint relation triplet extraction technology is vast. It significantly improves answer accuracy in intelligent question-answering systems, thereby serving as a crucial foundation for information extraction and knowledge graph construction. In personalized recommendation systems, it enhances user experience, enabling more precise recommendations. Furthermore, in dialogue systems, it bolsters context comprehension, resulting in more natural and fluid exchanges. In the realm of social network analysis, this technology aids in identifying interactions among users, providing valuable data support for marketing strategies [78]. In biomedical fields, it contributes to drug development by extracting key relations that drive scientific discoveries. Collectively, these applications not only accelerate the rapid advancement of relation triplet extraction technology but also lay a vital technological foundation for the intelligent transformation of various industries. As the field evolves, an increasing array of innovative applications is anticipated, highlighting its profound societal value and academic significance.

The future development prospects of joint relation triplet extraction undoubtedly brim with vast potential. On the technical front, the growing emphasis on interpretability and transparency is increasingly vital; this focus not only fosters user trust in the decision-making processes of models but also deepens the understanding of deep learning mechanisms. Simultaneously, the emergence of multimodal learning enables models to integrate diverse data sources such as text, images, and videos, thus enhancing their performance in navigating complex scenarios [79]. Furthermore, the incorporation of continuous learning capabilities is poised to become a crucial factor, allowing models to self-update and adapt flexibly to the rapidly changing landscape of knowledge and environments. In addition, combining few-shot learning and self-supervised learning with relation triplet joint extraction techniques has the potential to significantly enhance the generalization capabilities of relation extraction systems. This is particularly true in low-resource scenarios, where such approaches can improve the scalability of models, enabling them to perform effectively even when labeled data is scarce or difficult to obtain. Additionally, the adaptation to cross-domain and cross-language contexts is likely to emerge as a focal point of research, thereby broadening the applicability of these models across various fields and languages [80].

In terms of applications, the technology of relation triplet extraction is set to intertwine more closely with the construction of knowledge graphs, facilitating ongoing advancements in intelligent question-answering, personalized recommendations, social network analysis, and biomedical information extraction. This synergy is expected not only to optimize user experiences and enhance the efficiency of information retrieval and decision support but also to drive the intelligent transformation of sectors such as corporate decision-making, marketing, and scientific research. As technology continues to evolve and application scenarios diversify, relation triplet extraction is positioned to reveal its profound social value while igniting new avenues for exploration and innovation within academic research, thus underscoring its pivotal role in advancing the construction of a smart society.

This paper provides a brief review of joint extraction technologies for relational triples, categorizing them into two major approaches: joint decoding and parameter sharing. Joint decoding methods can be further divided into three types: table filling, tagging, and sequence-to-sequence. While joint decoding is effective for extracting triples from short sequence texts and allows for easier overall task model adjustments, its performance diminishes when dealing with long sequence texts and complex texts. On the other hand, parameter sharing methods enable the two subtasks of relational triple extraction to share some or all of the parameters, thereby reducing redundancy in model parameters. However, modifying shared parameters affects all tasks, thus limiting the model’s flexibility. With the continuous advancement of deep learning and natural language processing technologies, future research directions primarily focus on optimizing both the performance of joint decoding and parameter sharing methods across different text scenarios. Additionally, enhancing the extraction of cross-document relational triples and exploring few-shot learning and transfer learning will also be crucial for improving adaptability in low-resource languages and domains.