Abstract
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.
Highlights
Since the emergence of the concept of the Semantic Web, ontologies used in information science have been constructed by various agents across diverse domains to store and organize information
This section aims to demonstrate the superiority of the proposed frequent subgraph mining and movie rating prediction methodology
The experimental process is as follows: first, the frequent subgraphs for each user are extracted through training from the entire ontology graph data
Summary
Since the emergence of the concept of the Semantic Web, ontologies used in information science have been constructed by various agents across diverse domains to store and organize information. Ontology is a semantic data model that specifies concepts and the relationships between the concepts [1]. An ontology that defines the “type” of “things” and reveals the semantic relationship between the “things” is itself an enormous graph and a collection of various types of information. Experts in each field define a domain ontology to represent their knowledge about classes, properties, and instances in their point of view, according to W3C’s Web standards such as Resource Description Framework (RDF) and Web Ontology Language (OWL). A database that stores instance-level data using the graph structure of ontology (class/property relations) is a knowledge graph. We refer to such a knowledge graph as “ontology graph data” or “ontology-based knowledge graph” in our study
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