=Paper=
{{Paper
|id=Vol-3254/paper341
|storemode=property
|title=ForestQB: An Adaptive Query Builder to Support Wildlife Research
|pdfUrl=https://ceur-ws.org/Vol-3254/paper341.pdf
|volume=Vol-3254
|authors=Omar Mussa,Omer Rana,Benoît Goossens,Pablo Orozco-terWengel,Charith Perera
}}
==ForestQB: An Adaptive Query Builder to Support Wildlife Research==
https://ceur-ws.org/Vol-3254/paper341.pdf
ForestQB: An Adaptive Query Builder to Support
Wildlife Research
Omar Mussa1,3,* , Omer Rana1 , Benoît Goossens2 , Pablo Orozco-terWengel2 and
Charith Perera1
1
School of Computer Science and Informatics, Cardiff University, United Kingdom
2
School of Biosciences, Cardiff University, United Kingdom
3
College of Computing and Informatics, Saudi Electronic University, Saudi Arabia
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 construction to match the user requirements.
(Demo is available at https://iotgarage.net/demo/forestQB)
Keywords
Linked-Data, Visual Querying, SPARQL, Query Builders
1. Introduction
Publishing the data as a Linked-Data using Semantic Web technologies is beneficial for machine
learning as well as information retrieval [1]. 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 [2]. 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 knowledge extraction
out of the RDF triplestores by allowing the researchers to construct their query visually. The
The 21st International Semantic Web Conference, October 23–27, 2022, Hangzhou, China
*
Corresponding author.
$ mussao@cardiff.ac.uk (O. Mussa); ranaof@cardiff.ac.uk (O. Rana); goossensbr@cardiff.ac.uk (B. Goossens);
orozco-terwengelpa@cardiff.ac.uk (P. Orozco-terWengel); pererac@cardiff.ac.uk (C. Perera)
0000-0001-8614-6550 (O. Mussa); 0000-0003-3597-2646 (O. Rana); 0000-0003-2360-4643 (B. Goossens);
0000-0002-7951-4148 (P. Orozco-terWengel); 0000-0002-0190-3346 (C. Perera)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
tool provides a high level of abstraction for users by supporting a Form-based interface with an
integrated conversational AI to support natural query construction.
2. ForestQB Features
ForestQB is a web application that works on the browser to query and explore RDF triplestores
that were exposed as a SPARQL endpoint. ForestQB was implemented based on the requirements
collected by reviewing the current progress in the field and interviewing experts in the Bioscience
field (the stakeholder). The recent features of the tool can be outlined as follows:
• General overview: the initial interface provides a basic search functionality by allowing
the user to select sensors (observable properties) as a starting point to explore the data. The
interface contains a list of sensors loaded from the endpoint when the toolkit was initially
loaded. The sensors’ data are fetched by sending a SPARQL query that can be adjusted
from the settings to customise the tool if querying different endpoints. Hence, The user can
click the search button to start loading the results related to that sensor without requiring
additional steps. In addition, the interface includes a map with geospatial filtering capabilities
by drawing circles directly on the map. It also contains an upper date picker that will strict
the results to a general date range. The upper left side includes a list of predefined examples
that will populate all required fields to promote learning by example. The primary goal is to
have a user-friendly interface that will support the user to explore the data quickly.
• Detailed querying: once the users decide to have more advanced querying, they can click
on the search customisation to display all the detailed querying features. The interface will
display all the linked properties for the chosen sensor with an additional sublist allowing
them to manually add filters to each property. The filters that the user can add will vary
based on the property XSD data type. For example, a string will include Contain, Match and
Regex filters. Also, the same filters will act differently based on the property data type. For
example, the number and date both have a Range filter, but while the number will show a
numeric text field, the date will show a date picker. Furthermore, the user can choose to hide
the entity from the results or mark an entity as optional to be ignored if it does not exist.
• Conversational AI: the ForestQB includes a component that will display a chatbox where
the user can express their query in natural language to populate the corresponding fields
automatically. In addition to constructing queries, it can help the user to inquire about the
underlying data structure. For example, the chatbot can answer simple questions such as
"What are the sensors?", "What is Aqeela?" and "Where is Aqeela?" as shown in Figure 2. The
user can completely hide the form-based interface to use the chatbot as a stand-alone query
builder tool to query and retrieve the results.
3. Our SPARQL endpoint
The used dataset contains sensitive historical data collected by bioscientists that were modelled
as Linked-data. Thus, our SPARQL endpoint is privately available. The dataset populates
Figure 1: Overview of a simple query to retrieve data of a particular animal within a specific date and
location. The Longitude and Latitude of the animals were hidden due to the sensitivity of the data.
Forest Observatory Ontology (FOO) 1 , a novel ontology that describes wildlife data generated by
sensors. It was developed by reviewing the state-of-art and reusing entities from different mature
and self-contained ontologies. FOO arose from the efforts to review Open Data Observatories
[3] in order to build the Forest Observatory. The data integrates multiple ontologies to define
its underlying structure, including SOSA [4] ontology. Hence, ForestQB initially relies on SOSA
to get all connected sensors and their observable properties, which means it will potentially
work with another endpoint if it applies SOSA.
4. Demonstration
The demo will illustrate the ForestQB interface and explain its functionality. Figure 1 shows an
overview of the tool to create a simple query using map filters, demonstrating the basic search
with a limited number of filters to apply. The “customisation search” button is where the user
1
https://naeima.github.io/Forest-Observatory-Ontology/
Figure 2: Overview of the conversational AI (ForestBot). (a) Retrieving information about “Aqeela”
sensor. (b) Constructing a query to find “Aqeela”. (c) The result will then be reflected on the Query
Builder as if the user has selected them, and the search process will be triggered.
can apply a more advanced search. The customisation is always hidden until the user decides
to display it to allow a more neat look and hide the complexity of the interface. The results will
be presented in a tabular format to reflect the user query.
The conversational AI can work as an assistive tool to the Form-based builder or as a
standalone query builder. The user can type their query in natural language, and it will be
reflected on the interface. For example, a question such as “Where is aqeela?” will select the
correct sensor from the sensors list and trigger the search process (see Figure 2).
5. Technical Details
The ForestQB interface was designed after reviewing the current state-of-the-art and gathering
requirements from stakeholders. The design has numerous layers and components, each of
which serves a unique purpose with distinct technical details behind it. Thus, this section briefly
discusses some of the technical aspects of ForestQB.
• Web Application: Plain JavaScript and Vue.js (vuejs.org) Framework have been used to
build the tool as a Single-page application. The tool will generate a JSON object that will
be identical to the tool choices. Once the user clicks search, this object will be sent to the
conversion engine (as an AJAX request) to be converted to SPARQL and sent back to the tool
to be used for querying the endpoint. The separation of the SPARQL conversion mechanism
from the tool will allow it to be shared between the ForestQB and the conversational AI. Both
ForestQB and conversational AI share a centralised store using Vuex (vuex.vuejs.org).
• Conversational AI model: The conversational AI is split into two pieces: the front-end
and the classification model. The front-end is part of the main web application components.
However, the Natural Language Understanding (NLU) model has been built using RASA
(rasa.com) framework. Thus, all of the logic behind the NLU lies under a different web server
that is powered by RASA. The front-end will send all user messages to the chatbot server to
understand the user’s intent. Most of the actions (responses) are implemented within the
web front-end. The conversational AI will adjust the centralised store based on the classified
intent and entities to modify the JSON Query object. Then, it will trigger the search process.
The chatbot will either answer simple questions about the data schema or retrieve the results
when the user asks.
• Map Filters: ForestQB is offering map filters to narrow down the results. The implemented
map uses the Leaflet (leafletjs.com) library to visualise and allow the map’s main functionality.
Thus, drawing circles on the map allows the user to create nearby filters. Once a circle is drawn,
its coordinates and parameters are saved on the JSON Query object and then translated into
SPARQL. In addition, the user can define the relationship between the circles as intersections
or unions (as shown in Figure 1). As an adaptive feature, the map (GeoSPARQL) filters are
not applied to data that do not include Longitude and Latitude. However, the other type of
filters will still be valid.
6. Conclusions
This paper has briefly introduced the ForestQB, a toolkit that aims to assist bioscientists in
querying Linked-Data. The tool supports generating the query by mixing the conversational
AI with the Form-Based query builder. The ForestQB is still a work in progress, as we plan to
improve its conversational AI to handle more complex sentences. In addition, it will support
more visualisation options based on our future user study.
References
[1] S. Malyshev, M. Krötzsch, L. González, J. Gonsior, A. Bielefeldt, Getting the Most Out
of Wikidata: Semantic Technology Usage in Wikipedia’s Knowledge Graph, in: 17th
International Semantic Web Conference, volume 11137 LNCS, 2018, pp. 376–394.
[2] P. Warren, P. Mulholland, A Comparison of the Cognitive Difficulties Posed by SPARQL
Query Constructs, in: Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12387 LNAI,
Springer International Publishing, 2020, pp. 3–19.
[3] N. Hamed, O. Rana, P. Orozco-terWengel, B. Goossens, C. Perera, Open Data Observatories:
A Survey, Technical Report, Cardiff University, 2021. URL: https://orca.cardiff.ac.uk/id/
eprint/140048/14/NaeimaHamed_26_MAR_2021_V5.pdf.
[4] K. Janowicz, A. Haller, S. J. D. Cox, D. Le Phuoc, M. Lefrançois, SOSA: A lightweight
ontology for sensors, observations, samples, and actuators, Journal of Web Semantics 56
(2019) 1–10. URL: https://www.sciencedirect.com/science/article/pii/S1570826818300295.