# Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data
## 5 Conclusions  
This study first decomposed a large-scale ontology graph into small unit subgraphs, used these as units to calculate the frequency of the instance triples (or chunks), and made a chunk to find the frequent subgraphs. Next, the similarity between the rating graph and item graph was calculated via a Levenshtein distance-based method to predict the rating. The proposed approach was applied to an actual movie rating dataset and compared with other recommendation methods. According to the results, it outperformed the other approaches by approximately 40% in terms of accuracy and slightly outperformed them in terms of the F1-score, thus demonstrating that the proposed methodology can be useful in real applications. The main contributions of this study are as follows:  
1. This study presented a novel method of decomposing a large-scale knowledge graph into small unit subgraphs. These unit subgraphs serve as the criterion for occurrence counting, thereby enabling the frequency to be efficiently calculated. This approach of generating unit subgraphs to mine frequent subgraphs maintains the graph structure and meaning, which differentiates it from the method of simply creating transactions through an item set to apply an association rule mining algorithm. 2. The process starts with one instance triple and expands the subgraph through chunking. This chunking method makes it possible to find larger patterns while maintaining the structural information of the subgraph. By employing a method that utilizes instance triple paths, the subgraph can be expanded without being significantly affected by the size of the knowledge graph by performing chunking with a chunk and instance triple or between chunks. 3. Rather than merely extracting the frequent subgraphs, the methodology was applied to the application area of rating prediction. Its feasibility in providing recommendations through rating prediction was demonstrated, thus empirically proving the usefulness of frequent subgraphs. That is, the approach was shown to be effective when compared with other collaborative filtering algorithms, and a method for confirming the significance of the extracted frequent pattern was presented. 4. The proposed methodology can be used as a general recommendation engine extending the scope of the movie rating prediction. Information such as a user's purchasing history, areas of interest, and personal profiles acquired at the time of subscription can be represented in a graph, and then the proposed methodology can be used as a tool to analyze the buying patterns of other users with similar purchasing histories. In addition, the proposed methodology can also demonstrate high utilization in natural language processing such as complex questioning and natural language generation, and anomaly detection, which monitors and controls anomalies in real time by modeling complex structured networks, IT equipment and software in graph form, is a suitable field to show the performance of our methodology. On the other hand, our methodology is expected that in the bioscience area where the data involved is often represented by knowledge graphs, it will be able to demonstrate good performance in analyzing what proteins cause disease, what their relationship is, or whether certain drugs react to certain proteins in the human body and affect lesions. The extracted frequent subgraph maintains the original semantic information. The extensively researched rating prediction or recommendation approaches have the disadvantage of being unable to provide a reason or explanation for the result, which will likely be challenging to solve in future related studies as well. However, the methodology of this study has the significant advantage of being able to provide a clear answer to questions such as \"Why do you predict that I will give this movie a 4.0 rating?\" or \"Why are you recommending this movie to me?\"  
**Author Contributions:** Conceptualization, W.K., J.S.H. and K.L. ; methodology, W.K., J.S.H., H.J. and K.L. ; implementation, K.L. and H.J.