# A Knowledge Graph-Based Implicit Requirement Mining Method in Personalized Product Development
## 5 Multi-Category Personalized Implicit Requirement Mining Method Based on the Knowledge Graph
### Non-Co-Occurrence Implicit Requirement Mining of the Co-Reference Embedding Layer
#### 5.3.2 Construct Node Sequences by Biased Random Walk  
As shown in Figure 9, the Node2 Vec algorithm draws on the method of generating word vectors. It first starts from any point in the network based on a biased random walk and transfers between connected nodes. In this way, a limited nodes sequence can be collected, and the SkipGram model [47] is used to train the embedding vector E based on these sequences.  
The \"Biased Random Walk\" method proposed by Node2 Vec uses two parameters to adjust the random walk process. As shown in Figure 10, nodes V\({}_{5}\) and V\({}_{1}\) belong to the same node community, showing the homogeneity of nodes; while V\({}_{5}\) and V\({}_{8}\) belong to two different communities, but have the same structure (or role) in these two communities, showing the structural equivalence of nodes. In order to adjust the weights of these two structural characteristics in the algorithm, Node2 Vec generalizes DeepWalk [48]. By setting parameters, the bias of the breadth-first sampling strategy and the depth-first sampling strategy in the node sequence generation process can be selected to support the adjustment of the homogeneity and structural equivalence information reflected in the node embedding vector.  
Figure 8: Generation of co-occurrence networks based on the domain knowledge graph.  
Figure 9: Node2 Vec algorithm process.  
Specifically, Node2 Vec defines a random walk with parameters. Suppose a random walk has just walked through the edge (step 1 of Figure 10), and the current position is at node \(v_{4}\). The number on the arrow represents the step number during the search. Next, the random walk needs to make the next edge by evaluating the transition probability \(\pi_{v_{4}x}\). Let the transition probability be  
\[\pi_{v_{4}x}=\alpha_{pq}(v_{4},x)\cdot\omega_{v_{4}x} \tag{8}\]  
\[\alpha_{pq}(v_{4},x)=\begin{cases}\frac{1}{p}&if\ d_{v_{4}x}=0\\ 1&if\ d_{v_{4}x}=1\\ \frac{1}{q}&if\ d_{v_{4}x}=2\end{cases} \tag{9}\]  
where \(\omega_{v_{4}x}\) is the edge weight, and \(d_{v_{4}x}\) represents the shortest distance between \(v_{4}\) and \(x\). The parameters \(p\) and \(q\) can control the strategy of random walk exploration, where \(p\) controls the possibility of a walk returning to the previously reached node. Setting it to a large value ensures a more breadth-first walk, and a smaller \(q\) value encourages the depth-first strategy.  
3.3 Train the SkipGram Model to Obtain the Entity Embedding Vector, and Sort the Entities by Relevance  
Based on the collected node sequence set, a node is regarded as a word, and a node sequence is regarded as a sentence. Based on the SkipGram method of the Word2 Vec model, each node is represented as a low-dimensional dense vector \(E_{i}\).  
\[GraphSim_{Node2 Vec}(x,y)=\frac{E_{x}\cdot E_{y}}{\left\|E_{x}\right\|\left\|E_{ y}\right\|} \tag{10}\]  
When mining non-co-occurring implicit requirements, the formula can be used to calculate and sort the relevance of all entities, giving possible implicit requirement entities.