## 5 Conclusions  
This study comprehensively examines trends in knowledge graph (KG) research from 2022 to 2024, focusing on three main areas: extraction (2.x), learning (3.x), and evaluation (4.x). The findings indicate that KG research is progressing rapidly, driven by advancements in deep learning and natural language processing and the growing importance of hybrid approaches and domain-specific techniques. First, KG extraction (2.x) showed notable fluctuations in research interest over time. In 2022, foundational studies focused on improving basic models for extracting entities and relations from textual and unstructured data, as well as accelerating large-scale data preprocessing. In 2023, attention shifted toward multimodal and domain-specific extraction (Section 2.2.2), particularly in the context of large-scale patent and technical documents. By 2024, extraction research had taken another leap, focusing on automated pipelines capable \begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Name** & **Evaluation Metrics/Considerations** \\ \hline Electronics, Instacart & Assess product relationship extraction and recommender systems using Precision, Recall, F1-Score, and recommendation ranking metrics. \\ \hline MQALD & Evaluate natural language to SPARQL conversion by measuring query accuracy and response time. \\ \hline AMR2.0, AMR3.0 & Use parsing accuracy and F1-score to evaluate AMR parsing quality and structured semantic analysis. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. of handling complex contexts and multiple domains, resulting in a marked increase in more sophisticated methods. In the KG Learning (3.x) domain, the focus also varied by year. In 2022, enhancements to classical embedding methods such as TransE took center stage. Moving into 2023, Graph Neural Networks (GNNs) and Transformer-based models gained traction for tasks like node classification and link prediction. Then in 2024, there was a surge in novel methods that integrated large language models (LLMs) and applied hybrid approaches combining supervised and reinforcement learning (Section 3.5.3). These approaches enabled more accurate modeling of complex relational structures, significantly improving performance in specialized fields dealing with large-scale data, such as patents and technical reports. Lastly, KG Evaluation (4.x) peaked in 2023 before showing some decline in 2024. In 2022, intrinsic evaluation (Section 4.1) played a central role in establishing accuracy and precision metrics for early-stage models. In 2023, a spike was observed in extrinsic evaluation (Section 4.2) and Dataset/Domain-Specific Evaluation (Section 4.4), reflecting an increasing need to validate KG models for real-world applications in industry, healthcare, law, the Internet of Things (IoT), and more. While 2024 saw growing interest in interpretability and qualitative evaluation (Section 4.3), the overall research volume slightly decreased, likely due to concurrent efforts emphasizing quantitative metrics. Reflecting these year-to-year shifts in research priorities, this study provides a holistic review of the latest methods and trends in KG extraction, learning, and evaluation. In particular, 2022 featured substantial work on improving classical embeddings and intrinsic metrics, while 2023 saw an expansion into multimodal/domain-specific extraction, GNN/Transformer-based embeddings, and extrinsic evaluation. Subsequently, 2024 brought LLM integration, supervised + reinforcement learning hybrids, renewed attention to advanced extraction methods, and greater emphasis on interpretability and qualitative metrics. These developments highlight the simultaneous pursuit of broader applications and higher performance, implying that adaptive and integrated approaches are vital as data scales and complexities increase.