Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

Zhao Huang, Liu Yuan and Benjamin Miranda Tabak

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-18

Abstract: People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:2857611

DOI: 10.1155/2021/2857611

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