Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

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.

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

“…The core is an algorithm that iterative classifies objects in heterogeneous information networks to capture the information hidden in the semantics and structure of the graph. Lee et al [31] proposed a method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. The generated semantic information includes frequency counts for tasks such as rating prediction or recommendation.…”

Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs

“…An FSM algorithm [24] was applied to each user graph, chunking the overlapping parts until there were no more candidate patterns with a frequency over the minimum support. An isomorphic part to count the frequency was required, as the item instances included in the user graph were essentially all different.…”

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

“…In the same direction, Lee et al [11] seek to discover domain patterns across and within ontologies. However, to address this challenge, two different steps are adopted: a step where sub-graphs are extracted through candidate generation and chunking processes; a step where frequent sub-graphs mining [12] is adopted.…”