Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases
Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/4/450 |
| _version_ | 1797395516774940672 |
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| author | Gergely Honti János Abonyi |
| author_facet | Gergely Honti János Abonyi |
| author_sort | Gergely Honti |
| collection | DOAJ |
| description | Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network. |
| first_indexed | 2024-03-09T00:35:39Z |
| format | Article |
| id | doaj.art-98c8f045302346e88d654ddc11df3e80 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T00:35:39Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-98c8f045302346e88d654ddc11df3e802023-12-11T18:10:43ZengMDPI AGMathematics2227-73902021-02-019445010.3390/math9040450Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF DatabasesGergely Honti0János Abonyi1MTA-PE Complex Systems Monitoring Research Group, University of Pannonia, 8200 Veszprem, HungaryMTA-PE Complex Systems Monitoring Research Group, University of Pannonia, 8200 Veszprem, HungaryTriplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.https://www.mdpi.com/2227-7390/9/4/450multi-layer networkRDF storeDataToKnowledgeToNetworklinked data |
| spellingShingle | Gergely Honti János Abonyi Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases Mathematics multi-layer network RDF store DataToKnowledgeToNetwork linked data |
| title | Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases |
| title_full | Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases |
| title_fullStr | Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases |
| title_full_unstemmed | Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases |
| title_short | Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases |
| title_sort | frequent itemset mining and multi layer network based analysis of rdf databases |
| topic | multi-layer network RDF store DataToKnowledgeToNetwork linked data |
| url | https://www.mdpi.com/2227-7390/9/4/450 |
| work_keys_str_mv | AT gergelyhonti frequentitemsetminingandmultilayernetworkbasedanalysisofrdfdatabases AT janosabonyi frequentitemsetminingandmultilayernetworkbasedanalysisofrdfdatabases |