Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

ForestQB: Enhancing linked data exploration through graphical and conversational UIs integration

Mussa, Omar, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Goossens, Benoit ORCID: https://orcid.org/0000-0003-2360-4643, Orozco Ter Wengel, Pablo ORCID: https://orcid.org/0000-0002-7951-4148 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2024. ForestQB: Enhancing linked data exploration through graphical and conversational UIs integration. ACM Journal on Computing and Sustainable Societies 2 (3) , pp. 1-33. 10.1145/3675759

[thumbnail of ForestQB_Omar_Mussa_Manuscript.pdf]
Preview
PDF - Accepted Post-Print Version
Download (9MB) | Preview

Abstract

This paper introduces the Forest Query Builder (ForestQB), an innovative toolkit designed to enhance the exploration and application of observational Linked Data (LD) within the field of wildlife research and conservation. Addressing the challenges faced by non-experts in navigating Resource Description Framework (RDF) triplestores and executing SPARQL queries, ForestQB employs a novel integrated approach. This approach combines a graphical user interface (GUI) with a conversational user interface (CUI), thereby greatly simplifying the process of query formulation and making observational LD accessible to users without expertise in RDF or SPARQL. Developed through insights derived from a comprehensive ethnographic study involving wildlife researchers, ForestQB is specifically designed to improve the accessibility of SPARQL endpoints and facilitate the exploration of observational LD in wildlife research contexts. To evaluate the effectiveness of our approach, we conducted a user experiment. The results of this evaluation affirm that ForestQB is not only efficient and user-friendly but also plays a crucial role in eliminating barriers for users, facilitating the effective use of observational LD in wildlife conservation and extending its benefits to wider domains.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Biosciences
Publisher: Association for Computing Machinery (ACM)
ISSN: 2834-5533
Date of First Compliant Deposit: 3 June 2024
Date of Acceptance: 26 May 2024
Last Modified: 09 Oct 2024 08:41
URI: https://orca.cardiff.ac.uk/id/eprint/169440

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics