# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 2 Methodology
### Patent Knowledge Graph Reasoning Method Based on Graph Structure Feature Learning  
This paper addresses the limitations of traditional knowledge graph reasoning algorithms, which primarily focus on semantic information. By utilizing inductive learning to mine structural information within the knowledge graph, we propose a patent knowledge graph reasoning method based on graph structure feature learning, effectively enhancing  
Figure 2: Patent knowledge graph represented by a directed graph model. (Deep blue represents patent publication numbers, light blue represents inventors, dark green represents applicants, light green represents IPC patent numbers, and yellow represents domain keywords.) the accuracy of the model's reasoning. The structural design of the model is detailed as follows:  
1. Graph Input of the Algorithm: To enable computers to process the entities and relationships in the patent knowledge graph, which exist in textual data form, it is necessary to convert them into vector format. This paper employs knowledge graph embedding techniques to store entities and relationships in vectors for retention. 2. Acquisition of Semantic Information from the Knowledge Graph: To overcome uncertainties in the reasoning process, the paper utilizes the concept of Graph Convolutional Networks (GCNs) on top of knowledge graph embedding techniques to capture information within the knowledge graph. First, we establish a model of a knowledge graph that encompasses multiple relational types. The entire knowledge graph is denoted by \(\text{G}=(V,\varepsilon,R)\), within which the entity vectors are represented by \(\
u_{i}\in V\), and \(r\in R\) denotes the variety of relations \(r\) contained within the relationship type \(R\). \(\varepsilon\) represents the set of relationship instances, which corresponds to the edges of the graph. The triple relationship pair \(\big{(}v_{i},r,v_{j}\big{)}\in\varepsilon\) includes two distinct entities and one type of relationship. GCN, a differentiable information propagation model, has evolved from the principles of thermodynamic propagation. Typically utilized for knowledge graph link prediction, GCN is a variant of the foundational graph neural networks (GCNs) and is extensively applied in tasks such as knowledge graph link reasoning. It is adept at capturing node and edge information within the graph, enabling end-to-end learning and reasoning. Its message propagation model, as illustrated in Equation (1), delineates the process of information dissemination and feature representation updating among nodes within the graph structure:  
\[h_{i}^{(l+1)}=\sigma\Bigg{(}\sum_{m\in M_{i}}\phi_{m}\Big{(}h_{i}^{l},h_{j}^{l }\Big{)}\Bigg{)} \tag{1}\]  
Herein, \(h_{i}^{l}\) represents the entity variable, \(v_{i}\) is the value at the \(l\)-th layer of the hidden layer, \(h_{i}^{l+1}\) denotes the value of the entity variable \(v_{i}\) at the \(l+1\)-th layer of the hidden layer, \(M_{i}\) represents the set of neighboring nodes of \(v_{i}\), and \(\sigma(. )\) employs the ReLU activation function. The hidden value of a certain layer, through the weight matrix coefficient \(W^{l}\), undergoes a pairwise linear transformation to obtain an aggregated result of the neighbor nodes, denoted as \(\theta_{m}\).