## 4 Approach
### Interpretability  
Unlike traditional embedding methods, the DRKG achieves interpretability through a symbolic explicit multi-hop reasoning mechanism. Specifically, given a natural language question \(\mathcal{Q}\), its reasoning process can be formalized as a path search algorithm: \(\mathcal{P}=\bigcup_{k=1}^{k}\{(e_{k-1},r_{k},e_{k})|e_{k}\}\). This process strictly simulates the two-stage characteristics of human reasoning: First, the DRKG identifies the topic entity of the natural language question, then locates the entity in the knowledge graph, and obtains the information directly related to the entity. Subsequently, the information directly related to the entity and the query logic of the natural language question are subjected to path pruning based on attention weights to obtain the retrieval answer for this hop. After that, this action is repeated until all the query logic in the natural language question is fully covered, and then, the final answer can be obtained. This approach has an obvious advantage as well as a weak feature. On the one hand, the advantage of the DRKG lies in the fact that its traversal process leaves a complete and clear chain of reasoning evidence, which is in line with the process of human thinking and reasoning. At the same time, this clear chain of reasoning can be applied to more downstream tasks, such as judicial judgments and disease diagnoses; as for these tasks, the reasoning answer and reasoning process are equally important. On the other hand, compared with the embedding method, the disadvantage of the DRKG is that its inference time is longer. Assume that the single-hop retrieval delay is \(t\); then, \(T_{DRKG}=K\times t\) and \(T_{Emb}=|\mathcal{E}|\times d\), where \(K\) is the total number of reasoning steps, \(|\mathcal{E}|\) is the total number of entities, and \(d\) is the dimension of model. Moreover, the reasoning result of each step will affect the reasoning process of the next step. If there is an error in one of the intermediate steps of reasoning, it is very likely that the subsequent reasoning will be invalid. Of course, this problem is not completely unsolvable. We can conduct repeated training on the error-prone jump numbers in places where reasoning errors are likely to occur. To show the explainability of the DRKG model, we selected a three-hop question answering task for analysis, as shown in Figure 3. The left side of the figure shows the interaction process with the LLMS, while the right side shows the traversal process in the knowledge graph. The yellow nodes represent the topic entity of the question, the red paths represent interactions with the knowledge graph during reasoning, and the green nodes represent the final answer. The specific reasoning steps are as follows:  
Step 0: Identify the topic entity _[PERSON]_, and generate reasoning plans from the question semantics. Step 1: From the topic entity, retrieve all the relations _directed_by, has_tags, release_year, starred_actors, written_by, written, acted_in, tagged_with, and match the reasoning plan _Plan is Find the 'director' relationship of the '[PERSON]' entity in the knowledge graph to find the director entity_, selecting the _directed_by_ relation. _[PERSON]_ can then be retrieved from the knowledge graph. Figure 3: An example of interpretability for a three-hop knowledge question-answering. Step 2: Based on Step 1, retrieve all the relations of the entity _[PERSON]_ as a candidate list (_starred_actors, directed, written_by, acted_in, written_), and match the reasoning plan _Plan is Then find all movie entities corresponding to the 'directed' relationship of this director entity_, selecting the _directed_ relation. _Man with the Gun, It's All True, Invitation to a Gunfighter_ can then be retrieved from the knowledge graph.