### Survey Taxonomy  
Figure 1 provides a high-level overview of the proposed research framework for knowledge graph construction, visually representing the three core phases--Extraction, Learning, and Evaluation--along with their key sub-components. This figure is crucial not only for understanding the logical progression of each phase but also for situating the various methodologies discussed in this paper. The diverse methodologies presented in the main text are sometimes reiterated to help the readers better comprehend the content. By aligning the surveyed taxonomy with the structure shown in Figure 1, readers can easily understand how foundational extraction techniques, advanced machine learning approaches, and rigorous evaluation protocols collectively form a cohesive pipeline. In particular, the sub-stages within each phase (e.g., Pre-Construction Preparation for Extraction, Graph Neural Network-Based Learning, Qualitative Evaluation methods, etc.) reflect the positioning of the various research threads synthesized in this paper, thereby clarifying how each method and reference discussed in the following sections fits into the broader knowledge graph construction roadmap. **Extraction. ** Section 2 discusses the various processes involved in collecting and transforming raw data into structured data to construct knowledge graphs. This includes a range of extraction techniques, from rule-based and machine learning-based methods for foundational entity and relationship extraction to approaches that enhance extraction accuracy using LLMs (e.g., transformer-based models). By analyzing diverse methods, from traditional rule-based approaches to deep learning architectures, this section aims to provide an understanding of both conventional techniques and the latest advancements. **Learning. ** Section 3 focuses on machine learning techniques that infer and refine relational patterns within knowledge graphs. The discussion begins by exploring traditional embedding models and relation learning algorithms--such as TransE and DistMult--that provide a foundational framework for representing entities and their interconnections in a low-dimensional space. Building on these established methods, this section then delves into recent developments that utilize Graph Neural Networks, attention-based architectures, and contrastive learning techniques to capture complex interactions and higher-order structures. Additionally, it highlights advanced strategies, including transfer learning and various few-shot, one-shot, and zero-shot approaches that address data sparsity and enhance the generalization and robustness of the knowledge graph models. **Evaluation. ** Section 4 examines the various metrics and evaluation frameworks used to assess the performance of Knowledge Graphs (KGs). The evaluation process consists of intrinsic and extrinsic evaluations. Intrinsic evaluation measures the internal quality of a knowledge graph by focusing on three key aspects: First, accuracy evaluates whether the entities and relationships are correctly identified. Second, coverage indicates the extent to which a KG represents a relevant domain. Third, consistency ensures that the information in the KG is maintained without contradictions. In contrast, extrinsic evaluation measures how effectively a KG can be utilized in practical downstream tasks such as information retrieval and question answering. Furthermore, this section provides a comparative analysis of various evaluation benchmarks, including widely used datasets and challenge tasks, to comprehensively highlight the strengths and limitations of current approaches. In summary, this survey aims to integrate the recent research landscape surrounding knowledge graph construction into a coherent roadmap, providing a reference for researchers and practitioners seeking to transcend the current limitations of the field. By comprehensively exploring extraction, learning, enhancement, and evaluation, we aim to lay a foundation for future innovations.