Sequential Linked Data: the state of affairs

Tracking #: 2471-3685

This paper is currently under review
Authors: 
Enrico Daga
Albert Meroño

Responsible editor: 
Guest Editors Web of Data 2020

Submission type: 
Full Paper
Abstract: 
A sequence is a useful representation of many real-world phenomena, such as co-authors, recipes, timelines, and media. Consequently, sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have shown the data models that Knowledge Graphs commonly use for representing sequences; that these models have an impact in query performance; and that this impact is invariant to specific triplestore implementations. However, the specific list operations that management of Sequential Linked Data requires, beyond the simple retrieval of an entire list or a range of its elements, remain unclear. Besides, the impact of the different models in data management operations remains unexplored. Consequently, there is a knowledge gap on how to implement a real Semantic Web list Application Programming Interface (API) that enables standard list manipulation and generalizes beyond specific data models. In order to address these challenges, here we build on our previous work and propose a set of read-write Semantic Web list operations in SPARQL, towards the realization of such an API. We identify five classic list-based computer science sequential data structures (\textit{linked list}, \textit{double linked list}, \textit{stack}, \textit{queue}, and \textit{array}), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.
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