## 2 KGs Extraction
### Advanced Knowledge Graph Construction
#### 2.3.3 Entity and Relationship Alignment  
Recent studies addressing the problem of entity and relation alignment have proposed various approaches aimed at effectively modeling complex contextual information and mitigating noise. One notable model is the Dual Attention Graph Convolutional Network (DAGCN) [75], which combines local attention and global attention to capture both local structural information around entities (e.g., neighboring nodes, adjacent words, attributes, etc.) and long-range dependencies arising from global contextual and syntactic relationships within sentences. By modeling both local and global contexts, DAGCN achieves high accuracy for complex sentences, as shown by benchmarks like TACRED and SemEval.  
The Contextual Dependency-Aware GCN [76] integrates dependency-based graph structures with contextual information by segmenting sentences at the token level to build and process a dependency graph with a GCN. Unlike earlier methods that only consider simple dependency relations, it incorporates contextual embeddings to resolve polysemy and ambiguity, thereby enhancing entity and relation extraction, even in complex sentences.  
Another notable line of research focuses on extracting and aligning relationships between objects and their corresponding actions and states in knowledge graphs. A proposed approach [77] automatically identifies and connects object-action and object-state relationships in knowledge graphs--such as linking \"cup\" with \"drink\" or \"cup\" with \"fragile\". This method applies a sophisticated matching algorithm that enables the effective association of actions or states with objects, even in noisy or structurally complex contexts. By enriching the knowledge graph information with object-centric features and their associated attributes and actions, this approach enhances the graph's semantic richness and utility.  
Furthermore, an Interactive Optimization approach [78] has been introduced to incrementally improve model performance by incorporating continuous user or expert feedback during the relation extraction and alignment process. In this framework, misclassified cases or additional information identified during human inspection are fed back into the model, which adjusts its knowledge graph representation learning based on the feedback. Through repeated cycles of interaction and adjustment, the model gradually refines its alignment capabilities, enhancing its accuracy and reliability, even in complex domains where fully automated systems might struggle.  
In summary, approaches based on attention-based modeling, dependency graph integration, object-action/state extraction, and interactive feedback have significantly improved the accuracy and robustness of entity alignment and relationship alignment. Specifically, the DAGCN [75] and Contextual Dependency-Aware GCN [76] effectively integrate diverse contextual information, achieving superior performance even in complex sentence structures. The object-action/state extraction technique [77] robustly captures intricate relationships in noisy environments, while the Interactive Optimization approach [78] leverages continuous feedback to iteratively refine model performance, ensuring higher reliability in challenging domains.