Published Versions 1 Vol 2 (4) : 529–553 2020
Download
An RDF Data Set Quality Assessment Mechanism for Decentralized Systems
: 2018 - 12 - 27
: 2020 - 10 - 25
: 2020 - 03 - 20
428 11 0
Abstract & Keywords
Abstract: With the rapid growth of the linked data on the Web, the quality assessment of the RDF data set becomes particularly important, especially for the quality and accessibility of linked data. In most cases, RDF data sets are shared online, leading to a high maintenance cost for the quality assessment. This also potentially pollutes Internet data. Recently blockchain technology has shown the potential in many applications. Using the blockchain storage quality assessment results can reduce the centralization of the authority, and the quality assessment results have characteristics such as non-tampering. To this end, we propose an RDF data quality assessment model in a decentralized environment, pointing out a new dimension of RDF data quality. We use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment results. We have implemented a system DCQA to test and verify the feasibility of the quality assessment model. The proposed method can provide users with better cost-effective results when knowledge is independently protected.
Keywords: Decentralization; Quality assessment; Blockchain; RDF data set
Acknowledgements
This work was supported by the National Natural Science Foundation of China (U1836118, 61602350) and the Key Projects of National Social Science Foundation of China(11&ZD189). Thanks to graduate students Yansong Wang and Ren Hui for their professional guidance in the process of implementing the DCQA system in this paper, as well as Professor Lynda Hardman from Information Access Research Group, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, for her suggestions for modification and improvement of this paper.
[1]
S. Nakamoto. Bitcoin: A peer-to-peer electronic cash system. Available at: https://bitcoin.org/en/bitcoin-paper.
[2]
M. Swan. Blockchain: Blueprint for a new economy. Sebastopol, CA: O'Reilly Media, 2015. isbn: 9781491920497.
[3]
R. Beck, J. S. Czepluch, N. Lollike & S. Malone. Blockchain-The gateway to trust-free cryptographic transactions. In: The Twenty-Fourth European Conference on Information Systems (ECIS), 2016, pp. 1-14. Available at: http://aisel.aisnet.org/ecis2016_rp/153.
[4]
T. Berners-Lee, J. Hendler, & O. Lassila. The semantic web. Scientific American 284(5) (2001), 28-37. Available at: http://www.lassila.org/publications/2001/SciAm.pdf.
[5]
S. Decker, S. Melnik, F. Van Harmelen, D. Fensel, M. Klein, J. Broekstra, M. Erdmann & I. Horrocks. The semantic web: The roles of xml and rdf. IEEE Internet Computing 4(5)(2000), 63-73. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.231.4547&rep=rep1&type=pdf.
[6]
A. Newitz. Web 3.0. New Scientist 197(2647)(2008), 42-43. doi: 10.1016/S0262-4079(08)60674-0.
[7]
C. Bizer, R. Cyganiak, & T. Heath. How to publish linked data on the web. Available at: http://wifo5-03.informatik.uni-mannheim.de/bizer/pub/LinkedDataTutorial/.
[8]
A. Hogan, J. Umbrich, & A. Harth, R. Cyganiak, A. Polleres & S. Decker. An empirical survey of linked data conformance. Journal of Web Semantics 14(2012), 14-44. doi: 10.1016/j.websem.2012.02.001.
[9]
J. Kandari. Information quality on the World Wide Web: A user perspective. PhD dissertation, University of Nebraska-Lincoln, 2010. Available at: doi: 10.1504/IJIQ.2011.043784.
[10]
A. Assaf & A. Senart. Data quality principles in the semantic web. In: IEEE Sixth International Conference on Semantic Computing, 2012, pp. 226-229. doi: 10.1109/ICSC.2012.39.
[11]
A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann & S. Aue. Quality assessment for linked data: A survey. Semantic Web 7(1)(2016), 63-93. Available at: http://www.semantic-web-journal.net/system/files/swj773.pdf.
[12]
C.Bizer & R. Cyganiak. Quality-driven information filtering using the WIQA policy framework. Journal of Web Semantics, 7(1)(2009), 1-10. doi: 10.2139/ssrn.3199414.
[13]
D. Magazzeni, P. McBurney & W. Nash. Validation and verification of smart contracts: A research agenda. Computer 50(9)(2017), 50-57. doi: 10.1109/MC.2017.3571045.
[14]
R C. Merkle. Protocols for public key cryptosystems. In: 1980 IEEE Symposium on Security and Privacy. IEEE, 1980: 122-122. doi: 10.1109/SP.1980.10006.
[15]
SPARQL. Available at: https://www.w3.org/TR/sparql11-query/.
Article and author information
Cite As
L. Huang, Z. Liu, F. Xu & J. Gu. An RDF data set quality assessment mechanism for decentralized systems. Data Intelligence 2(2020), 529–553. doi: 10.1162/dint_a_00059
Li Huang
L. Huang is the leader of this work. She designed the RDF data quality assessment mechanism and DCQA system framework in a decentralized system. She made meaningful contributions to the revision of the paper.
Li Huang is currently an associate professor of computer science of the College of Computer Science and Technology, Wuhan University of Science and Technology. She received her PhD in computer science from Huazhong University of Science and Technology in 2011. Her research interests include data management and semantic Web and knowledge.
Zhenzhen Liu
Z. Liu summarized the methodology part of this paper. He made meaningful contributions to the revision of the paper.
Zhenzhen Liu is currently a postgraduate student in the College of Computer Science and Technology, Wuhan University of Science and Technology. He received a bachelor’s degree from Wuhan University of Science and Technology in 2017. His research interests include semantic Web, knowledge graph and linked data quality assessment.
Fangfang Xu
F. Xu summarized the methodology part of this paper. She made meaningful contributions to the revision of the paper.
Fangfang Xu received her B.S. degree from the College of Computer Science and Technology at Wuhan University of Science and Technology (WUST) in 2012 and the M.S. degree from College of Computer Science and Technology at WUST in 2015. Now she is an engineer at the College of Computer Science and Technology, WUST. Her research interest is semantic computing.
Jinguang Gu
J. Gu (simon@wust.edu.cn) as the corresponding author summarized and drafted this paper. He made meaningful contributions to the revision of the paper.
Jinguang Gu is currently a professor of the College of Computer Science and Technology, Wuhan University of Science and Technology (WUST) and vice dean of Institute of Big Data Science and Engineering, WUST. He received his PhD in software theory from Computer School of Wuhan University in 2005. His research interests include knowledge graph, semantic Web, anddistributed and service computing.
Publication records
Published: Dec. 17, 2020 (Versions1
References
Data Intelligence