# Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network
## 3 Unified Data Modeling in the Cultural Heritage Knowledge Collection Stage for Design
### Key Technology Research  
The construction of the design knowledge information data model encompasses several stages: knowledge modeling, knowledge storage, knowledge extraction, knowledge fusion, knowledge computation, and knowledge application. This study employs a bottom-up construction method, focusing on the design knowledge collection stage within the entire design process to build a preliminary design knowledge graph ontology framework. Based on the diverse data within design knowledge information, knowledge representation is carried out to assist in knowledge extraction, knowledge fusion, and knowledge storage. Among these stages, knowledge extraction and knowledge fusion are critical steps in constructing a knowledge graph. Using machine learning methods, we designed techniques for knowledge extraction and knowledge fusion and stored the resulting data. Knowledge extraction technology involves the automatic identification and extraction of structured knowledge from textual data. This includes entity recognition, relationship extraction, and attribute extraction, which are used to construct knowledge graphs, databases, or other knowledge management systems. For example, in the sentence _The gilded tortoise-pattern silver plate, peach-shaped, features a gilded tortoise in the center_, entities such as _gilded tortoise-pattern silver plate_ and _tortoise_ can be extracted. The relationship between these entities is _decorates_, and the attribute of the _gilded tortoise-pattern silver plate_ entity is _gold and silver artifact_, describing the shape attribute of the _gilded tortoise-pattern silver plate_ entity. Figure 1: Unified data modeling for knowledge information based on knowledge graphs. In this extraction task, the attribute extraction task is redefined as a problem that combines entity recognition and relationship extraction. The core of this method lies in not only identifying entities in the text but also recognizing attribute values associated with these entities and linking these attribute values to the corresponding entities through specific relationships. Therefore, the focus of knowledge extraction should be on entity recognition and relationship extraction. Structured data typically employs manually mapped rules. For data with an explicit structure, such as tables in databases, predefined rules can directly map entities and their relationships within the data. These rules are manually created based on the structural characteristics of the data and are used to identify and extract specific information. Semistructured data generally uses wrapper induction methods to identify and standardize the source code paths of the information to be extracted, which is used to extract information from semistructured web data. Design knowledge information, such as traditional cultural knowledge, often comprises unstructured data. Unstructured data (such as text files, text in images, etc.) is converted into an editable format through text recognition and other technologies. Specialized recognition techniques are needed to extract knowledge information from this data, identifying specific entities, relationships, and more. Due to the generally low quality of most design knowledge information data, this study proposes an improved text information extraction method, namely an entity-relationship joint extraction method based on a segmental attention fusion mechanism. During the process of entity recognition and relationship extraction, the method fully considers the contextual information in the text data, enhancing the accuracy of entity recognition. The text is divided into multiple segments and entity recognition and relationship extraction are independently performed for each segment, addressing the issue of overlapping entity relationships. After extracting knowledge from the text, it is necessary to merge entities with the same name. This is because the same name might refer to different entities or the same entity might be referred to by different names in various contexts.