Please use this identifier to cite or link to this item:
Title: Minimal type inference for Linked Data consumers
Authors: Ciobanu, Gabriel
Horne, Ross
Sassone, Vladimiro
Keywords: Linked Data
Type systems
Operational semantics
Issue Date: 2014
Source: Ciobanu, G., Horne, R., & Sassone, V. (2014). Minimal type inference for Linked Data consumers. Journal of Logical and Algebraic Methods in Programming, 84(4), 485-504.
Series/Report no.: Journal of Logical and Algebraic Methods in Programming
Abstract: We provide an introduction to the Web of Linked Data from the perspective of a Web developer who would like to build an application using Linked Data. We identify a weakness in the development stack, namely a lack of domain specific scripting languages for designing background processes that consume Linked Data. To address this weakness, we design a scripting language with a simple but appropriate type system. In our proposed architecture, some data is consumed from sources outside of the control of the system and some data is held locally. Stronger type assumptions can be made about the local data than about external data, hence our type system mixes static and dynamic typing. We prove that our type system is algorithmic; and can therefore be used for minimal type inference. We also prove subject reduction and type safety results, which justify our claim that our language is statically type checked and does not throw basic runtime type errors. Throughout, we relate our work to the W3C recommendations that drive Linked Data, so that our syntax is accessible to Web developers.
ISSN: 2352-2208
DOI: 10.1016/j.jlamp.2014.12.005
Schools: School of Computer Engineering 
Rights: © 2014 Elsevier Inc. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Logical and Algebraic Methods in Programming, Elsevier Inc. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
rdfs.pdf185.38 kBAdobe PDFThumbnail

Citations 50

Updated on Feb 27, 2024

Web of ScienceTM
Citations 50

Updated on Oct 30, 2023

Page view(s) 50

Updated on Feb 27, 2024

Download(s) 50

Updated on Feb 27, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.