Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

Applied Sciences

Haemin Jung¹  Wooju Kim¹  Kwangyon Lee¹  JuneSeok Hong² 

[1] Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea;Department of Management Information Systems, Kyonggi University, Gyeonggi-do 16227, Korea;

关键词: frequent subgraph mining;    knowledge graph;    semantic web;    ontology;    rating prediction;    recommendation;   

DOI  :  10.3390/app11030932

来源: DOAJ

【 摘 要 】

In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.

【 授权许可】

Unknown