Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies
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 challenge...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/2857611 |
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| author | Zhao Huang Liu Yuan |
| author_facet | Zhao Huang Liu Yuan |
| author_sort | Zhao Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f2e1767d79384ea98ef1336c90310e80 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-f2e1767d79384ea98ef1336c90310e802025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/28576112857611Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain OntologiesZhao Huang0Liu Yuan1Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an, ChinaPeople 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.http://dx.doi.org/10.1155/2021/2857611 |
| spellingShingle | Zhao Huang Liu Yuan Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies Discrete Dynamics in Nature and Society |
| title | Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies |
| title_full | Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies |
| title_fullStr | Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies |
| title_full_unstemmed | Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies |
| title_short | Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies |
| title_sort | understanding large scale social relationship data by combining conceptual graphs and domain ontologies |
| url | http://dx.doi.org/10.1155/2021/2857611 |
| work_keys_str_mv | AT zhaohuang understandinglargescalesocialrelationshipdatabycombiningconceptualgraphsanddomainontologies AT liuyuan understandinglargescalesocialrelationshipdatabycombiningconceptualgraphsanddomainontologies |