# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 4 Conclusions  
This paper addresses the issue of incomplete patent knowledge graphs, which impacts the accuracy of patent knowledge graph application systems, by establishing a directed graph representation model for patent knowledge graphs. It proposes a reasoning algorithm based on graph structural feature learning and integrates a self-attention mechanism. This algorithm significantly improves the accuracy of reasoning outcomes such as unknown patent classification, potential relationships between entities, and new knowledge, demonstrating the strong reasoning capabilities of the patent knowledge graph reasoning method that combines semantic and structural features when dealing with complex structural knowledge graphs. This method can effectively complete and refine the patent knowledge graph.  
In the future, patent knowledge graph inference methods that combine semantic and structural features hold extensive potential for practical applications, including patent novelty assessment, patent retrieval systems, patent technology analysis systems, and technology transfer and licensing.  
Patent Novelty Assessment: The proposed inference algorithm, which integrates graph neural networks and self-attention mechanisms, can uncover latent relationships between patents and identify novel technology combinations and innovations.  
Patent Retrieval Systems: The enhanced inference algorithm can significantly improve the accuracy and efficiency of patent retrieval systems.  
Patent Technology Analysis Systems: The inference algorithm proposed in this paper can more accurately infer patent classifications, relationships between inventors, and latent knowledge within patents.  
Technology Transfer and Licensing: By employing the proposed inference method, potential opportunities for technology transfer and licensing can be better identified and analyzed, facilitating the commercialization of innovative results.  
Additionally, we will explore strategies to better handle complex cases of polysemy and synonymy, further analyze the model's performance across different types of queries or relationships, and enhance the extraction of structural feature information.  
The following considerations are essential for subsequent research:  
1. Introduce a context-aware embedding mechanism to improve handling of lexical polysemy.
2. Optimize graph structure parsing algorithms to enhance understanding of complex entities and relationships, especially in dealing with homonymy.
3. Integrate natural language processing technologies to enhance the semantic expressive capabilities of knowledge graphs.  
Conceptualization, L.Z., K.H. and X.M.; Methodology, L.Z., K.H. and X.M.; Software, L.Z. and K.H.; Validation, L.Z., K.H. and X.S.; Formal analysis, K.H. and X.M.; Investigation, X.S.; Resources, L.Z.; Data curation, X.S.; Writing--original draft, L.Z. and K.H.; Writing--review & editing, X.M.; Visualization, X.S.; Supervision, X.M.; Funding acquisition, X.M.. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Shanghai's 2023 \"Technology Innovation Action Plan\" soft science research project (grant no. 23692102300).  
The raw data supporting the conclusions of this article are available within the article. Data link: [[https://pan.baidu.com/s/lbWYmBXFf1](https://pan.baidu.com/s/lbWYmBXFf1) WaucUkqFDczw?pwd=lned]([https://pan.baidu.com/s/lbWYmBXFf1](https://pan.baidu.com/s/lbWYmBXFf1) WaucUkqFDczw?pwd=lned). Accessed 28 July 2024.  
The author declares that there is no conflict of interest regarding the publication of this paper.