Harmse, HenrietteBritz, KatarinaGerber, Aurona J2019-03-072019-03-072018-09Harmse, H., Britz, K. and Gerber, A.J. 2018. Informative armstrong RDF datasets for n-Ary relations. Frontiers in Artificial Intelligence and Applications, pp. 187-199978-1-61499-909-6DOI: 10.3233/978-1-61499-910-2-187http://www.ebooks.iospress.com/volumearticle/50257http://hdl.handle.net/10204/10748© 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)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.enABoxData miningDBPediaExample dataInformative ArmstrongInformative Armstrong RDF datasetn-ary relationSemantic WebUniqueness constraintBook ChapterHarmse, 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/10204/10748Harmse, 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.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.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 -