Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Abstract

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.

Landscape domains and information surfaces: Data collection, recording and citation using decimal latitude‐longitude geolocation via the FAIR principles

“…Engineering geomorphological classifications are visual ('reading the ground') as well as related to earth materials (Hutchinson, 2001) where fieldwork, mapping and visualising (Hungr et al, 2014) are important parts of 'understanding' (Fookes et al, 2000;Griffiths, 2014), perhaps related to Cézanne's conceptualizations of Montagne Sainte-Victoire (Elderfield, 2020). I now examine some visualisation examples within information landscape and landsystem approaches before looking more closely at linking geomorphic data to organise, store and share data with context (Honti & Abonyi, 2021). (Brighenti et al, 2019) as well as species adaptations to cold environments (Ornaghi et al, 2023) is required.…”

“…Multi-layered network interdisciplinary sustainability and climate change projects, in which geomorphology was a significant component, have been examined by Honti and Abonyi (2021). Their analysis took account of data hierarchies to examine linked data (LD) and the resource description framework (RDF) data model.…”

Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production

“…Studies are published to formalise the bipartite graphs as an intermediate model for RDF with a goal of graph-based notions in querying and storage [82]. Furthermore, an RDF database can be simultaneously analyzed as layers of a multilayer network [83], providing a solution for production flow analysis.…”

The Formal Graph of APRDF

“…Ontology as graph uses a graph model for ontology matching [21] [22], an essential step in data integration. Although it only works for simple graphs, the formal graph as a bipartite graph can represent the layer of the network [23]. A formal graph helps the ranking problem for a few definitions in work [24] by Hayes et al [10].…”