# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 1 Introduction
Constructing a knowledge graph containing all knowledge and information requires a massive workload. However, in patent knowledge graph inference algorithms, patent data mining provides substantial information support, significantly enhancing the accuracy and effectiveness of these algorithms. [PERSON] et al. (2019) developed a technology intelligence system based on attributes and functions, utilizing patent data mining to identify technological trends. [PERSON] et al. (2019) combined patent analysis with bibliometric techniques to propose a method for constructing a knowledge graph of technological development, aiding in the identification and prediction of emerging technological trends. [PERSON] et al. (2019) proposed a keyword-based technology roadmap method to construct technology roadmaps, thereby guiding new product development and technological innovation. Similarly, in the patent knowledge graph field, reasoning algorithms continually iterate to achieve higher performance standards. For example, [PERSON] et al. (2019) used extracted features to train a Support Vector Machine (SVM), effectively improving the F-score for relevance, but further optimization of inference capability is needed. [PERSON] et al. (2019) proposed a method based on Semantic Maximum Divergence Measure (SMDM), demonstrating excellent improvements in F1 scores and external metrics, but it overlooked the extraction of structural information. [PERSON] et al. (2019) introduced a Knowledge-Driven Convolutional Neural Network (K-CNN), and experiments showed that this model outperformed the current state-of-the-art relation extraction models, yet it performed poorly on large-scale datasets. [PERSON] et al. (2019) integrated a multi-head attention mechanism into Graph Neural Networks, proposing an attention-based knowledge graph inference GNN, which enhanced the accuracy of inference tasks but focused solely on the analysis of semantic information in the graph. Although most of the inference models in the aforementioned studies have improved inference speed and accuracy to varying degrees through innovative approaches, they still struggle to fully utilize the information within and surrounding the nodes in knowledge graphs. Moreover, methods based on traditional neural networks and graph neural networks predominantly focus on enhancing the acquisition of semantic information from the graph while neglecting the structural information. Consequently, they lack the ability to infer knowledge in unlearned domains. To address the aforementioned issues, this paper first utilizes patent data mining techniques to automatically identify necessary content. This provides rich data support for knowledge graph inference algorithms. On this basis, a directed graph representation model of the patent knowledge graph is constructed, thus establishing the fundamental framework of the patent knowledge graph. Building on this, a reasoning method for patent knowledge graphs (SS-DSA) that integrates semantic and structural features is proposed. This method enhanced the application scope of the knowledge graph. By integrating semantic and structural features and incorporating a self-attention mechanism, the accuracy of the inference results is significantly improved. This supports complex reasoning tasks while markedly enhancing the inference outcomes, as illustrated in Figure 1. This process further refines the content and reasoning capabilities of the patent knowledge graph. Additionally, the improved patent knowledge graph inference methods can enhance the value and utility of patent knowledge graphs by uncovering potential connections between patents, identifying novel technological combinations and innovation points, pinpointing technological breakthroughs and development directions, and inferring relationships between different technological fields. This study proposes a patent knowledge graph inference method that combines semantic and structural features (SS-DSA). First, a directed graph representation model of the patent knowledge graph is designed.