ForestQB An Adaptive Query Builder to Support Wildlife Research Omar Mussa13 Omer Rana1 Benoît Goossens2 Pablo Orozco-terWengel2

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ForestQB: An Adaptive Query Builder to

Support Wildlife Research

Omar Mussa1,3, Omer Rana1, Benoît Goossens2, Pablo Orozco-terWengel2,

and Charith Perera1

1School of Computer Science and Informatics, Cardiﬀ University, United Kingdom

{mussao, ranaof, pererac}@cardiff.ac.uk

2School of Biosciences, Cardiﬀ University, United Kingdom

{goossensbr, orozco-terwengelpa}@cardiff.ac.uk

3College of Computing and Informatics, Saudi Electronic University, Saudi Arabia

o.mousa@seu.edu.sa

Abstract. This paper presents ForestQB, a SPARQL query builder, to

assist Bioscience and Wildlife Researchers in accessing Linked-Data. As

they are unfamiliar with the Semantic Web and the data ontologies,

ForestQB aims to empower them to beneﬁt from using Linked-Data

to extract valuable information without having to grasp the nature of

the data and its underlying technologies. ForestQB is integrating Form-

Based Query builders with Natural Language to simplify query construc-

tion to match the user requirements.

Keywords: Linked-Data ·Visual Querying ·SPARQL ·Query Builders.

Paper type: Demo (available at https://iotgarage.net/demo/forestQB)

1 Introduction

Publishing the data as a Linked-Data using Semantic Web technologies is ben-

eﬁcial for machine learning as well as information retrieval [2]. While the data

will be easily accessible by machines, Humans can also beneﬁt from accessing

the data by using a query language such as SPARQL, which is the recommended

query language for querying RDF triplestore.

In the ﬁeld of Bioscience and Wildlife conservation, researchers tend to collect

data using various sensors such as temperature, location and speed. Therefore,

hundreds of gigabytes were collected over the years that would be extremely

valuable if stored as a knowledge graph in an RDF triplestore. However, users

usually feel intimidated to use Linked-Data as they are obliged to understand

SPARQL and the underlying data structure [3]. In order to encourage these

Bioscience researchers to adopt semantic web technologies in their ﬁeld, it is

essential to present a toolkit that fulﬁls their requirements to freely access the

data store without the need to worry about its underlying technology.

In this demo, we introduce ForestQB, a tool that aims to facilitate the knowl-

edge extraction out of the RDF triplestores by allowing the researchers to con-

struct their query visually. The tool provides a high level of abstraction for users

arXiv:2210.02640v1 [cs.IR] 6 Oct 2022

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ForestQB:AnAdaptiveQueryBuildertoSupportWildlifeResearchOmarMussa1;3,OmerRana1,BenoîtGoossens2,PabloOrozco-terWengel2,andCharithPerera11SchoolofComputerScienceandInformatics,CardiUniversity,UnitedKingdom{mussao,ranaof,pererac}@cardiff.ac.uk2SchoolofBiosciences,CardiUniversity,UnitedKingdom{goossen...

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ForestQB An Adaptive Query Builder to Support Wildlife Research Omar Mussa13 Omer Rana1 Benoît Goossens2 Pablo Orozco-terWengel2

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