Learning Categories from Linked Open Data

Chen, Jesse Xi; Reformat, Marek

doi:10.1007/978-3-319-08852-5_41

Cited by 11 publications

(2 citation statements)

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“…This section presents the relevant background for classifying and comparing the schemes for scheme discovery and methods. The analysis carried out in this section is primarily based on review papers [1]- [4] in which state-of-the-art schema information extraction approaches are presented. These studies analyzed the influences of the approach to scheme discovery, techniques for scheme discovery, characterization of discovery procedures, input and output data formats, and quality of the results.…”

Section: Related Workmentioning

confidence: 99%

Restoration of Data Structures Using Machine Learning Techniques

Cvijetić,

Radivojević

2023

IEEE Access

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Tabular data is the most common format used to represent real-world information. Almost all programs created for storing or processing data, such as relational database systems, spreadsheets, and statistical analysis software can import or export tabular data. These programs are not sufficiently robust to automatically solve the problems of importing messy delimited files or files that contain data from multiple tables. Additional messy datasets contain data delimited by multiple delimiters without the names of the table columns, and parts of the table rows have substituted or deleted columns. This paper proposes the STCExtract algorithm for reconstructing table structures and data in which the input file can be arranged. The STCExtract algorithm is designed to be domain-independent and modular according to machine learning algorithms and other parameters. The algorithm was developed as a two-phase process, in which the original data tables were recognized in the first phase and the columns of the original data tables in the second phase. The STCExtract algorithm was evaluated through expensive experiments using multiple real datasets. Multiple messy datasets were generated for the four experiments. Three experiments were conducted to determine the optimal parameters for the STCExtract algorithm. A fourth experiment was conducted to evaluate the proposed algorithm. The results show that the STCExtract algorithm correctly arranged the structure of the tables with an accuracy of 94.4% to 100%. The accuracy of the STCExtract algorithm in the second phase (when the data were allocated to columns) ranged from 59.7% to 90.2%.

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Section: Related Workmentioning

confidence: 99%

Restoration of Data Structures Using Machine Learning Techniques

Cvijetić,

Radivojević

2023

IEEE Access

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“…The persistent disk-based storage is a way to store RDF data permanently on file system by using the most influential indexing techniques, such as B+ tree (Singh et al 2018), AVL (Wood et al 2005) and B-tree (Harth and Decker 2005). Among the existing solutions we can mention (Broekstra et al 2002;Aasman 2006;Harth et al 2007;Kolas et al 2009;Tran et al 2009;Chen and Reformat 2014). It is important to notice that, reading from and writing to disks slow the search process to an unacceptable level and induce an important performance bottleneck (Curé and Blin 2014).…”

Section: Native Systemsmentioning

confidence: 99%

An Empirical Study on the Evaluation of the RDF Storage Systems

Mahria

Chaker

Zahi

2021

Preprint

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In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieve highly competitive computational performance against other state-of-the-art methods in terms of Load Time and Query Response Time.

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