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http://dx.doi.org/10.5392/JKCA.2022.22.08.033

Approximate Top-k Labeled Subgraph Matching Scheme Based on Word Embedding  

Choi, Do-Jin (창원대학교 컴퓨터공학과)
Oh, Young-Ho (충북대학교 빅데이터협동과정)
Bok, Kyoung-Soo (원광대학교 인공지능융합학과)
Yoo, Jae-Soo (충북대학교 정보통신공학부)
Publication Information
The Journal of the Korea Contents Association / v.22, no.8, 2022 , pp. 33-43 More about this Journal
Abstract
Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.
Keywords
Labeled Graph; Approximate Subgraph Matching; Top-k Matching; Learning Model;
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