dc.contributor.author |
Harmse, Henriette
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|
dc.contributor.author |
Britz, Katarina
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dc.contributor.author |
Gerber, Aurona J
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dc.date.accessioned |
2019-03-07T09:47:13Z |
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dc.date.available |
2019-03-07T09:47:13Z |
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dc.date.issued |
2018-09 |
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dc.identifier.citation |
Harmse, H., Britz, K. and Gerber, A.J. 2018. Informative armstrong RDF datasets for n-Ary relations. Frontiers in Artificial Intelligence and Applications, pp. 187-199 |
en_US |
dc.identifier.isbn |
978-1-61499-909-6 |
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dc.identifier.uri |
DOI: 10.3233/978-1-61499-910-2-187
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dc.identifier.uri |
http://www.ebooks.iospress.com/volumearticle/50257
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dc.identifier.uri |
http://hdl.handle.net/10204/10748
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dc.description |
© 2018 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0) |
en_US |
dc.description.abstract |
The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that, for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: The structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IOS Press |
en_US |
dc.relation.ispartofseries |
Worklist;22130 |
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dc.subject |
ABox |
en_US |
dc.subject |
Data mining |
en_US |
dc.subject |
DBPedia |
en_US |
dc.subject |
Example data |
en_US |
dc.subject |
Informative Armstrong |
en_US |
dc.subject |
Informative Armstrong RDF dataset |
en_US |
dc.subject |
n-ary relation |
en_US |
dc.subject |
Semantic Web |
en_US |
dc.subject |
Uniqueness constraint |
en_US |
dc.title |
Informative armstrong RDF datasets for n-Ary relations |
en_US |
dc.type |
Book Chapter |
en_US |
dc.identifier.apacitation |
Harmse, H., Britz, K., & Gerber, A. J. (2018). Informative armstrong RDF datasets for n-Ary relations., <i>Worklist;22130</i> IOS Press. http://hdl.handle.net/10204/10748 |
en_ZA |
dc.identifier.chicagocitation |
Harmse, Henriette, Katarina Britz, and Aurona J Gerber. "Informative armstrong RDF datasets for n-Ary relations" In <i>WORKLIST;22130</i>, n.p.: IOS Press. 2018. http://hdl.handle.net/10204/10748. |
en_ZA |
dc.identifier.vancouvercitation |
Harmse H, Britz K, Gerber AJ. Informative armstrong RDF datasets for n-Ary relations.. Worklist;22130. [place unknown]: IOS Press; 2018. [cited yyyy month dd]. http://hdl.handle.net/10204/10748. |
en_ZA |
dc.identifier.ris |
TY - Book Chapter
AU - Harmse, Henriette
AU - Britz, Katarina
AU - Gerber, Aurona J
AB - The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that, for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: The structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.
DA - 2018-09
DB - ResearchSpace
DP - CSIR
KW - ABox
KW - Data mining
KW - DBPedia
KW - Example data
KW - Informative Armstrong
KW - Informative Armstrong RDF dataset
KW - n-ary relation
KW - Semantic Web
KW - Uniqueness constraint
LK - https://researchspace.csir.co.za
PY - 2018
SM - 978-1-61499-909-6
T1 - Informative armstrong RDF datasets for n-Ary relations
TI - Informative armstrong RDF datasets for n-Ary relations
UR - http://hdl.handle.net/10204/10748
ER -
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en_ZA |