# Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network
## 5 Experiment Validation  
### Dataset Creation  
To validate the effectiveness of the model in handling entity relations, we will conduct tests on both Chinese and English datasets. For the Chinese dataset, we will use the Tang Dynasty Gold and Silver Artifacts Knowledge Text Dataset. In the collection of artifact knowledge aimed at design, the preliminary stage requires acquiring a large amount of artifact knowledge information, filtering out irrelevant texts,and retaining key descriptions of the artifact's shape, decoration, craftsmanship, and cultural origin. To achieve this goal, we need to first gather a substantial amount of textual information. The unstructured data are based on authoritative historical materials and literature related to Tang Dynasty gold and silver artifacts, which nearly cover all significant collections of Tang Dynasty gold and silver artifacts [3; 4; 5; 37; 38; 39; 40; 41; 42; 43; 44; 45]. These materials offer comprehensive studies from the perspectives of archaeology and history, providing detailed descriptions of various aspects, such as the artifacts' shapes and decorations, thereby enriching the dataset's corpus. Scholar [PERSON]'s book \"Study of Tang Dynasty Gold and Silver Artifacts\" offers detailed descriptions of the functions and series of gold and silver artifacts, laying the foundation for the dataset's categorization. The selected artifacts in the dataset include not only everyday containers but also Buddhist artifacts unearthed from the Famen Temple underground place, such as offering vessels and ritual implements. These gold and silver artifacts, mostly offered by Tang emperors [PERSON] and [PERSON], exhibit high aesthetic value in their shapes and decorations due to their royal origin. Therefore, the gold and silver artifacts unearthed from the Famen Temple underground place are also an important part of the dataset. According to the research of scholars, analyzing the fundamental properties of artifacts includes their function, technology, appearance, and symbolic meaning. These summarize the basic information and interpretation of cultural relics, which facilitates a deeper understanding of their knowledge. During the text training process, we use the ChatGPT-4 model for data augmentation. Traditional data augmentation methods (such as synonym replacement, random deletion, etc.) have limitations in generating text with accuracy and diversity and still require manual annotation in many application scenarios [46; 47; 48; 49; 50; 51; 52]. Multiple studies have shown the powerful capability of ChatGPT in text data augmentation, significantly improving classification performance in few-shot learning [53]. The availability of unstructured text data enhances the model by integrating dimensions of usability, determinants, and rules. Through methods such as synonym replacement, text expansion, and adding or rewriting contextual information, the dataset is enhanced while preserving the original entity relationships. Based on modules of shape, decoration, craftsmanship, and cultural connotation of Tang Dynasty gold and silver artifacts, structured triple information is used to annotate the text, defining entity categories and relationship categories. This process ultimately constructs the Tang Dynasty Gold and Silver Artifacts Knowledge Text Dataset. Using structured triple information to label the text, defining entity categories and relationship categories, the Tang Dynasty Gold and Silver Artifacts Knowledge Text Dataset is finally constructed. The dataset is illustrated in Table 1, taking the gilt silver cup with musical performance patterns as an example. It includes the shape, decoration, decorative techniques, parts of the artifact, and cultural origin of the gilt silver cup with musical performance patterns, covering most relationship categories of Tang Dynasty gold and silver artifacts. The English dataset used is the New York Times (NYT) dataset. The NYT dataset comes from actual news articles with annotated entities and relationships and additional information.