Pipeline for Ontology Construction Using a Large Language Model: A

Smart Campus Use Case

Daniel Lichtnow

1 a

, Ana Marilza Pernas Fleischmann

2 b

, Leonardo Vianna do Nascimento

3 c

Guilherme Medeiros Machado

4 d

and Jos

e Palazzo Moreira de Oliveira

5 e

Universidade Federal de Santa Maria (UFSM), Santa Maria/RS, Brazil

Universidade Federal de Pelotas (UFPel), Pelotas/RS, Brazil

Instituto Federal de Educac¸

ao, Ci

encia e Tecnologia do Rio Grande do Sul (IFRS), Alvorada/RS, Brazil

LyRIDS lab, ECE Engineering School, Paris, France

Universidade Federal do Rio Grande do Sul (UFRGS), Instituto de Inform

atica - PPGC, Porto Alegre/RS, Brazil

Keywords:

Semantic Web, Large Language Model (LLM), Smart Campus, Ontology Patterns.

Abstract:

Developing Semantic Web ontologies is a complex endeavor that necessitates a deep understanding of a spe-

ciﬁc domain, proﬁciency with Semantic Web patterns, the use of ontology editors, and the exploration and

reuse of relevant existing ontologies. This paper presents a pipeline for ontology construction, leveraging a

Large Language Model (LLM). Our work intends not to create a new methodology for ontology construction,

but to explore how these tools can assist in the ontology-building process, acknowledging that they may not

fully automate it. The pipeline was designed through an experience report, following the steps outlined in a

recognized ontology construction guide to ensure a degree of reproducibility. A complex use case, a Smart

Campus, was chosen to illustrate this process. This experience paper aims to highlight new possibilities while

addressing the challenges encountered.

1 INTRODUCTION

The development of ontologies remains a complex

and labor-intensive process. It requires a deep un-

derstanding of the domain in question, careful con-

sideration of the relationships between concepts, and

meticulous attention to detail. The challenges of on-

tology construction highlight the need for ongoing re-

search and innovation to develop more efﬁcient meth-

ods, ultimately enabling broader and more effective

use of ontologies in computing. The work of (Neto,

2024) shows an analysis of the evolutionary path

traced by the semantic web over the past two decades,

highlighting the challenges observed in attempting

to bring Berners-Lee’s (Berners-Lee et al., 2001) vi-

sion into practice. Large Language Models (LLMs)

and their applications, like ChatGPT (Generative Pre-

https://orcid.org/0000-0003-0103-0538

https://orcid.org/0000-0001-8142-817X

https://orcid.org/0000-0002-2197-3106

https://orcid.org/0000-0001-5283-9228

https://orcid.org/0000-0002-9166-8801

trained Transformer)

and Gemini

, have revolution-

ized the ﬁeld of knowledge engineering by automat-

ing tasks that were once solely the domain of human

experts. While not a complete replacement for skilled

data engineers, generative AI is a powerful assistant.

Our research aims to identify how LLMs can facil-

itate the deﬁnition of ontologies, although they may

not completely automate the process. The intention

of our work is not to create a new methodology for

ontology construction. In this sense, the pipeline was

structured according to steps delineated in Ontology

Development 101 (Noy and McGuinness, 2001). In

this paper, each of the steps outlined in Methodology

101 has been applied with the support of ChatGPT,

with the aim of developing an experience report based

on the use of this LLM as support for deﬁning an on-

tology. For this experience, we chose a case study that

requires knowledge across multiple domains for on-

tology creation: a Smart Campus, where technologies

are integrated with physical infrastructure and learn-

https://openai.com/chatgpt/

https://gemini.google.com/

Lichtnow, D., Fleischmann, A. M. P., Vianna do Nascimento, L., Machado, G. M. and Moreira de Oliveira, J. P.

Pipeline for Ontology Construction Using a Large Language Model: A Smart Campus Use Case.

DOI: 10.5220/0013096000003929

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 97-104

ISBN: 978-989-758-749-8; ISSN: 2184-4992

ing resources to improve services, decision-making

and learning experience. This paper is structured as

follows. Section 2 presents relevant aspects of the

ontology concept, Semantic Web, Large Language

Models, and works related to our proposal. Section 3

presents the methodology of Ontology Development

101 (Noy and McGuinness, 2001) and our experience

report applied to the use case - a smart campus on-

tology. Section 4 presents a set of learned lessons re-

lated to the proposed ontology deﬁnition process and

discusses the results and future work.

2 RELATED WORKS AND

CONCEPTUAL BACKGROUND

In the ﬁelds of Computer Science and Information

Science, ontology refers to a formal representation

of knowledge, typically organized as a set of con-

cepts within a speciﬁc domain and the relationships

between them. Ontologies serve to deﬁne shared vo-

cabularies and establish a common understanding of

a domain, thereby enhancing communication and en-

suring interoperability across different systems and

applications. They are often expressed using for-

mal languages such as RDF (Resource Description

Framework) or OWL (Web Ontology Language). The

process of ontology building has been systematized

by several studies, already well-established and ref-

erenced in the ﬁeld (Kotis et al., 2020). It is not the

focus of this work to conduct an in-depth study on

methodologies for ontology development, but rather

to identify the methodology that would best suit the

proposed experience report. Since we propose a new

approach to ontology development, we chose a sim-

ple methodology, easy to use, broad spectrum, non-

collaborative that is widely referenced and commonly

applied in practice for ontology development, such

as Methodology 101 (Noy and McGuinness, 2001)

More current in research today, LLMs like BERT

(Devlin et al., 2018), ROBERTA (Liu et al., 2019),

or advanced ones like ChatGPT, consist of machine

learning models that memorize facts and knowledge

contained in training corpus (Petroni et al., 2019).

Utilized for natural language processing/generation,

they are indeed very powerful. However, studies

reveal that LLMs often struggle to recall facts and

may even experience hallucinations by generating un-

true statements. Additionally, since they represent

knowledge implicitly within their parameters, inter-

preting or validating their knowledge becomes chal-

lenging, resulting in a lack of interpretability (Pan

et al., 2024). Considering these issues, the area of

Retrieval-Augmented Generation (RAG) has emerged

as a promising solution by incorporating knowledge

from external databases (Gao et al., 2023). It is also

important to consider that the quality of the outputs

generated by an LLM is closely related to the qual-

ity of the prompts provided by the user (White et al.,

2023). LLMs, such as ChatGPT and Gemini, contain

knowledge about several domains and can be viewed

as a huge knowledge base. They present an inter-

face based on NLP to interact with their users. Con-

sidering its characteristics, our question is: could an

LLM replace and act as an expert system to support

the ontology building?. The use of LLMs for build-

ing ontologies is a novel approach. There is an in-

creasing number of works focused on using LLMs

for ontology development. Next, we present some ex-

amples. The work of (Meyer et al., 2023) presents

experiments that generated knowledge graphs from

semi-structured textual data, translated natural lan-

guage questions into syntactically correct and well-

structured SPARQL queries for some given knowl-

edge graphs, and even generated overview diagrams

for large knowledge graph schema (such as DBPe-

dia). A detailed analysis revealed that the gener-

ated results contain mistakes, of which some are sub-

tle. For some use cases, this might be harmless

and can be tackled with additional validation steps.

The work of (Rodrigues et al., 2023) investigates the

use of ChatGPT to classify domain terms accord-

ing to categories of two upper-level ontologies. The

work of (Babaei Giglou et al., 2023) proposes the

LLMs4OL approach, which utilizes LLMs for On-

tology Learning, which involves automatically ex-

tracting and structuring knowledge from natural lan-

guage text. To test this hypothesis, the authors con-

ducted a comprehensive evaluation using the zero-

shot prompting method and evaluated nine different

LLM model families for three main OL tasks: term

typing, taxonomy discovery, and extraction of non-

taxonomic relations. The obtained empirical results

show that foundational LLMs are not sufﬁciently suit-

able for ontology construction that entails a high de-

gree of reasoning skills and domain expertise. Nev-

ertheless, when effectively ﬁne-tuned they just might

work as suitable assistants, alleviating the knowledge

acquisition bottleneck, for ontology construction. A

fact-checking approach to ChatGPT is described in

(Mountantonakis and Tzitzikas, 2023). The approach

uses ChatGPT to generate RDF triples that describe

the information typed by the user in a prompt. These

triples are validated using a knowledge graph such

as DBPedia. Other examples include (Helskyaho,

2023) which focuses on reusing existing ontologies,

addressing only part of the ontology-building pro-

cess, and (Funk et al., 2023), which concentrates on

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

constructing a concept hierarchy for a given domain,

starting from a seed concept and considering only

is-a relations, thus also addressing only part of the

ontology-building process. Despite the growing in-

terest in leveraging Large language models (LLMs)

for ontology development, our review did not iden-

tify any works that explicitly integrate established

methodologies, such as Ontology 101, into their ap-

proaches. We have observed that the majority of these

works cover only part of the ontology building pro-

cess, rather than the complete process. This gap high-

lights an opportunity to explore how such methodolo-

gies can enhance the systematic application of LLMs

in ontology engineering.

3 APPLYING ONTOLOGY

DEVELOPMENT 101

THROUGH LLMs TO A USE

CASE

To enable a standardized approach, we base this work

on Ontology Development 101 (Noy and McGuin-

ness, 2001) methodology, which is used by many

works (now 8,399 citations in Google Scholar) and

is cited in Prot

e Website - an ontology editing en-

vironment with support for the Web Ontology Lan-

guage (OWL)

. The process deﬁned in Methodology

101 consists of 7 steps, which are described in the

following use case. The implementation of Methodol-

ogy 101 is often challenging due to the extensive con-

textual and terminological knowledge required, which

varies depending on the speciﬁc application domain.

Thus, this work investigates an alternative approach

to overcoming these challenges by integrating LLMs

into the Ontology Development 101 process. It is

important to highlight that we do not advocate us-

ing LLMs as the primary source for providing def-

initions and conceptualization. Our intention is to

demonstrate that using LLMs can be beneﬁcial. How-

ever, external sources with deﬁnitions are indispens-

able (e.g. see types of properties in step 5 where we

use the deﬁnitions of (Noy and McGuinness, 2001)

and step 2 where we observed ChatGPT’s inability to

identify relevant ontologies). Next, we describe the

use of Ontology Development 101 in a use case with

the help of an LLM. We describe the process from

steps 1 to 6, excluding the seventh step, which relates

to instance creation. In each stage, we aim to present

the results and explain what was done to obtain better

responses using ChatGPT.

https://protege.stanford.edu/support.php

3.1 Use Case - Smart Campus

A Smart Campus represents a higher education en-

vironment where smart technologies are seamlessly

integrated with physical infrastructure to enhance

the quality of services, improve decision-making

processes, and optimize resource utilization. A criti-

cal component of the Smart Campus concept is the

effective exchange of information between different

systems and agents within the campus. As these

technologies interact, the information they share must

be represented in a way that is understandable to all

participating systems. This highlights the justiﬁcation

for employing ontologies and the Semantic Web.

Step 1. Determine the Domain and Scope of the

Ontology. Some questions are proposed in this step:

1. What is the domain that the ontology will cover?

2. For what we are going to use the ontology?

3. For what types of questions the information in the

ontology should provide answers?

Thus, we start with question 1 and formulate the

question: What is a smart campus? The aim is

to identify the information about Smart Campus

gathered and produced by ChatGPT and obtain

more knowledge about the domain. The answer

produced by ChatGPT is aligned with the smart

campus concept previously presented in this section:

“A smart campus is an educational institution that

utilizes various technologies and digital infrastruc-

ture to enhance the learning experience, improve

operational efﬁciency, and foster a more connected

and sustainable environment. These technologies

can include Internet of Things (IoT) devices, sensors,

data analytics, and communication networks.” After,

based on the 2nd question, we formulate the follow-

ing question: For what we are going to use a Smart

Campus ontology? The answer was: “An ontology

for a Smart Campus would serve as a structured

representation of all the entities, concepts, and

relationships relevant to the campus environment,

particularly in the context of IoT (Internet of Things)

and smart technologies.” A key point of this answer

is related to the fact that learning aspects are not

explicitly stated. In this sense, in (Maran et al.,

2023), the authors observe that many works on Smart

Campus do not consider (or emphasize) learning as-

pects. The ChatGPT answer seems to reproduce this

scenario. Because of this, we formulate the following

question, omitting the word “smart”: For what are

we going to use a Campus ontology? The answer

was similar to the one obtained with the previous

question, but now it is possible to identify references

Pipeline for Ontology Construction Using a Large Language Model: A Smart Campus Use Case

to learning: “A Campus ontology is typically used

to model the various entities, relationships, and

attributes within a university or college campus envi-

ronment in a structured and organized manner. This

ontology can be utilized for a variety of purposes,

including [...] Enabling semantic web applications

that utilize campus-related data in innovative ways,

such as campus navigation systems, personalized

learning environments, or recommendation systems

for courses and activities. [...]” Finally, we formulate

a new question based on the 3rd question: For what

types of questions the information in the smart cam-

pus ontology should provide answers? The answer

was “[...] In summary, the smart campus ontology

should provide answers to questions related to navi-

gation, resource availability, events, safety, facilities

management, environmental conditions, security,

administrative matters, campus amenities, academic

support, and sustainability initiatives”. Now, even

using Smart Campus expression, learning aspects

are considered. We believe that the responses could

be especially valuable, particularly for individuals

with limited knowledge of the subject matter because

these answers offer a more detailed description and

contribute to a clearer understanding of the context.

Step 2. Consider Reusing Existing Ontologies.

Following Ontology Development 101, we formulate

the question: Are there ontologies for Smart Campus?

The answer was: “Yes, there are ontologies designed

speciﬁcally for Smart Campuses [...] ”. However,

in (Maran et al., 2023), we point out that there is no

speciﬁc ontology for the smart campus. Considering

this, we propose a new question: Give me examples

of these ontologies. The answer is “Certainly!

Here are a few examples of ontologies developed

for smart campus environments:...”. The examples

given by ChatGPT consist of a set of ontologies

that can be used not just in a smart campus context,

for example: Smart-M3 Ontology, Linked Sensor

Middleware Ontology (LSMO), Semantic Sensor

Network Ontology (SSN), CityPulse Ontology, and

Smart Campus Framework Ontology (SCFO). From

these examples of ontologies, the last one is not

as generic as the others. We have tried to locate

these ontologies using Google. We found some

of them, but we haven’t found the Smart Campus

Framework Ontology (SCFO), which seems to be

the most relevant for our work. Therefore, we

ask GPT: Where can I obtain the Smart Campus

Framework Ontology (SCFO)? The answer was: “As

of my last update, there isn’t a widely recognized

or standardized ontology called ”Smart Campus

Framework Ontology (SCFO)” in the academic or

research community. However, such an ontology

may have been developed by a speciﬁc research

group, organization, or company for their smart

campus projects”. Regarding this, refer to the lessons

learned in relation to Step 2, as discussed in Section 4.

Step 3. Enumerate Important Terms. About this

step, Noy and McGuinness (2001) afﬁrm “ Initially,

it is important to get a comprehensive list of terms

without worrying about the overlap between concepts

they represent, relations among the terms, or any

properties that the concepts may have, or whether

the concepts are classes or slots”. Considering that

ChatGPT gathered knowledge from many sources,

a fact that represents knowledge produced by many

stakeholders and experts, we formulated the fol-

lowing question: Enumerate important terms that I

can use for deﬁning a smart campus ontology. The

ChatGPT answer had 20 terms. Additionally, just like

in Step 1, when we employed the expression ’smart

campus,’ the answer does not emphasize learning

aspects. Thus, we decided to reformulate the question

using a simpliﬁed strategy of Retrieval-augmented

Generation (RAG). The simpliﬁed strategy consists

of supplying the question with text retrieved from

other sources. Using the concept of a Smart Campus

from (Muhamad et al., 2017), the question sent to

ChatGPT was: The basic idea of a smart campus is

an effort to integrate a set of advanced intelligence

technologies by the university to improve perfor-

mance, the quality of graduates, and the ease of life

through the provision of information technology ser-

vices that are valuable, dynamic, and user-oriented

to support automation and real-time reporting. This

encompasses not only learning activities but also

broader aspects such as social interaction, environ-

ment, ofﬁce management, energy saving, etc. Based

on this text, enumerate important terms that I can use

for deﬁning a smart campus ontology. The answer

had only 15 terms, but now the term ‘Learning’

appears: Smart Campus, Advanced Intelligence

Technology, Performance Improvement, Quality of

Graduates, Information Technology Services, Au-

tomation, Real-Time Reporting, Learning Activities,

Social Interaction, Environmental Management,

Ofﬁce Management, Energy Saving, User-Oriented

Services, Dynamic Services, Ontology.” We tested a

set of questions to increase the number of terms in

an answer specifying the number of terms. Thus, the

new question was: A smart campus is an educational

institution that utilizes various technologies and digi-

tal infrastructure to enhance the learning experience,

improve operational efﬁciency, and foster a more

connected and sustainable environment. Based on

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

100

this text, enumerate the 100 most important terms I

can use to deﬁne a smart campus ontology. Now we

have obtained 100 terms, but we decided to improve

the question to obtain terms related to distinct aspects

of a smart campus. Thus, we formulated a new

question, emphasizing that we desire terms related to

learning places, people, and resources. The question

was: A smart campus is an educational [...]. Based

on this text, enumerate the 100 most important terms

I can use to deﬁne a smart campus ontology. Take

into account places, people, and resources related

to learning. The answer produced by ChatGPT

was: “[...] This list covers a wide range of terms

relevant to deﬁning the ontology of a smart campus,

encompassing technology, infrastructure, learning

resources, people, and places.” See the answer

Step 4. Deﬁne the Classes and the Class Hierar-

chy. In the response produced by ChatGPT in Step

3, the agent itself identiﬁed the categories of tech-

nology and infrastructure for the listed terms. In our

instruction, we speciﬁed “Take into account places,

people, and resources related to learning” without

mentioning these terms. Therefore, in Step 4, we con-

sidered the following ﬁve categories: technology, in-

frastructure, learning resources, people, and places,

and the set of terms generated in Step 3. To deﬁne

classes, we decided to ask the following: “Please redo

and classify these terms into technology, infrastruc-

ture, learning resources, people, and places”. The

answers consist of a list of terms classiﬁed into 5

classes. In the next prompt, we asked ChatGPT to

“organize the terms into a class hierarchy to compose

the ontology of the smart campus”. From the cate-

gorization obtained, we identiﬁed the need for some

reﬁnements. Due to the domain complexity and lack

of space to describe the process, we focused our next

steps of the analysis on just part of the hierarchy –

the class “Places”. Here the ChatGPT produces more

than we have asked and categorizes places concepts in

eight categories: Smart Buildings, Campus Facilities,

Recreational Areas, Innovation and Learning Spaces,

Administrative Ofﬁces, Event Spaces, Student Hous-

ing, and Campus Grounds. We observed that a major

issue was the Classroom being considered a subclass

of Buildings. We made several requests to improve

the hierarchy: (i) Smart Classrooms and Classrooms

are not buildings; (ii) Laboratories, Ofﬁces, and Ad-

ministrative Ofﬁces are not subclasses of Building;

(iii) replace “Parking Management” with “Parking”.

The ﬁnal hierarchy obtained from ChatGPT for places

is partially shown in Figure 1. We repeat the process

https://chat.openai.com/share/8c219c59-6070-410d-

b818-163990d62ae8

for People, Technology, Infrastructure, and Learning

Resources. One important point is that it is necessary

to give the list of terms previously generated to Chat-

GPT and then ask for creating classes and subclasses.

Step 5. Deﬁne the Properties of Classes—Slots:

Such as Synonyms, Deﬁnitions, and Relationships

In Ontology Development 101, the authors suggest

that classes and their properties can be deﬁned us-

ing terms identiﬁed in Step 2. However, in our spe-

ciﬁc use case, we ﬁnd it challenging to follow this

approach because we are using all terms identiﬁed in

Step 3 to create the class hierarchy. Thus, we decide

to ask ChatGPT to generate properties for classes. We

did this by asking: “[we give the class hierarchy] +

For each class of this hierarchy, please generate prop-

erties”. However, the answer does not generate some

types of properties (e.g. relationships with other in-

dividuals). Thus, using text from (Noy and McGuin-

ness, 2001), we ask ChatGPT to redo the generation

of properties trying to include for each class intrinsic,

extrinsic, and relationship properties

. Some of the

classes with properties generated are shown in Fig-

ure 2.

Step 6. Deﬁne the Facets of the Slots. It is now

possible to generate the ontology using Semantic Web

Resources. We do this separately for Places, People,

Technology, Infrastructure, and Learning Resources.

The following prompt was passed to ChatGPT: “Now,

using each class and properties, generate an ontology

using OWL and RDF”. Here, ChatGPT has only

generated some classes. Therefore, we asked Chat-

GPT to redo the ontology using all classes and prop-

erties. However, obtaining the entire ontology in a

single response is impossible because, according to

ChatGPT, The character limit for a single response is

approximately 4096 characters.If a response exceeds

this limit, it can be split into multiple parts to en-

sure that all information is provided. Subsequently,

we downloaded each response segment and compiled

them into a single ﬁle. Another issue arises from

our decision to initiate a new chat for generating the

OWL/RDF ﬁle. This led to problems; for instance,

SmartRoom reverted to being a subclass of Building.

We addressed this problem by editing the ontology us-

ing Prot

e. We generated versions of OWL and RDF

for Places, People, Technology, Infrastructure, and

Learning Resources separately. We compiled them

into a single ﬁle.

https://chat.openai.com/share/15df29e3-c465-4d54-

abcd-b63047cd08e6

Pipeline for Ontology Construction Using a Large Language Model: A Smart Campus Use Case

101

Figure 1: Final hierarchy for concepts related to places.

Figure 2: Class with properties - relationships.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

102

4 LESSONS LEARNED

The process of creating a smart campus ontology with

Large Language Models (LLMs) is particularly useful

for evaluating various possibilities and challenges as-

sociated with using LLMs such as ChatGPT for this

task. In this context, we share some lessons learned

that can assist individuals undertaking this task and

facilitate the development of a pipeline for generating

ontologies. Following the learned lessons:

• Providing concepts, whether from external

sources or generated by the LLM itself, proved to

be useful in our case. Asking LLMs for concepts

can be useful especially for Step 3, where relevant

terms need to be identiﬁed. However, LLMs

cannot be the only source of knowledge. It is

necessary to provide deﬁnitions from external

resources—for example, deﬁnitions from a

paper using a simpliﬁed Retrieval-Augmented

Generation (RAG) strategy.

• Regarding Step 2, asking about existing on-

tologies is useful, but verifying their existence

and availability through search mechanisms and

querying ChatGPT itself is equally important.

The same applies to vocabularies. This is be-

cause, as reported, initial responses from Chat-

GPT even included non-existent ontologies. A

possibility that could be used in future wors is the

use of a feature called ChatGPT Search, which

enables ChatGPT

to access up-to-date content

from the internet and use Chain-of-Veriﬁcation

(CoVe) method (Dhuliawala et al., 2023).

• In Step 3, where relevant terms for the ontology

are identiﬁed, it proved useful to provide the con-

cepts obtained in Step 1 and establish a quanti-

tative goal, such as a number of terms. In our

case, we arbitrarily requested 100 terms, but this

approach will certainly require further evaluation

in the future.

• In Step 4 initially requesting term categorization,

followed by creating hierarchies and reﬁning them

incrementally, as done with the Places class, sim-

pliﬁes the process. In Step 3, it may be beneﬁcial

to request terms related to ontology classes, pro-

viding deﬁnitions for each class beforehand.

• In Step 5, requesting properties of classes while

providing deﬁnitions and examples of types (us-

ing deﬁnitions from Methodology 101) can be

beneﬁcial. Again LLMs cannot be the only source

of knowledge.

https://help.openai.com/en/articles/9237897-chatgpt-

• ChatGPT operates in a stateless manner, it does

not retain a memory or a persistent state between

individual interactions. This is a problem for on-

tology construction (certainly a task that takes

many days). However, we have noted that it is

better to use the same chat whenever possible, be-

cause in a single session or ongoing chat, Chat-

GPT can maintain context from previous interac-

tions (can recall what has been discussed during

the same conversation as it unfolds). However,

once the conversation ends and a new session be-

gins, ChatGPT starts a new one and does not re-

tain information from the previous session. One

possibility is to use Letta

• In Step 5, we believe that generating OWL and

RDF ﬁrst and then relating them to existing on-

tologies is a step that needs further reﬁnement. in

this sense, after enumerating terms for the ontol-

ogy in Step 3, we tried to link them to well-known

vocabularies such as DBPedia or the SSN ontol-

ogy, and the LLM made, in some cases, correct

relations. It could be interesting to make reﬁne-

ments and extend the ontology importing these

vocabularies. However, integrating these ontolo-

gies represent a persistent challenge. Achieving

seamless interoperability between diverse systems

often requires considerable effort in aligning and

mapping ontologies, addressing discrepancies be-

tween different standards, and ensuring consistent

interpretation of the deﬁned vocabularies (Amini

et al., 2024). This is a future work.

• It would be beneﬁcial to share the chat among

those who are deﬁning the ontology. However,

this is not possible in ChatGPT, where only one

individual can interact with the LLM.

4.1 Final Remarks

This paper described the use of a Large Language

Model (LLM) for creating an ontology following On-

tology Development 101. Rather than producing a

ﬁnal version of an ontology, the aim was to assess

how an LLM can provide support for developing a

pipeline. It would have been important to compare

the results of deﬁning the same ontology with and

without the proposed pipeline. However it should be

considered that there is no ontology similar to the one

we are attempting to develop in the case study (Smart

Campus) (Maran et al., 2023) In future work, we in-

tend to build upon the lessons learned by deepening

our understanding in several areas: (i) Establishing

more effective relationships between the ontology and

https://www.letta.com/

Pipeline for Ontology Construction Using a Large Language Model: A Smart Campus Use Case

103

existing ones; (ii) Exploring methods to overcome the

statelessness of an LLM; (iii) Developing strategies

for creating relationships between classes within the

generated ontology; (iv) Generating smaller and less

complex ontologies than the current work and im-

proving the ontology validation process.

ACKNOWLEDGEMENTS

This research is partially supported by CNPq/MCTI

Nº 10/2023 - UNIVERSAL grant n. 402086/2023-6

and by CNPq grant 306695/2022-7 PQ Sr.

REFERENCES

Amini, R., Norouzi, S. S., Hitzler, P., and Amini, R. (2024).

Towards complex ontology alignment using large lan-

guage models. arXiv preprint arXiv:2404.10329.

Babaei Giglou, H., D’Souza, J., and Auer, S. (2023).

Llms4ol: Large language models for ontology learn-

ing. In International Semantic Web Conference, pages

408–427. Springer.

Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The

semantic web. Scientiﬁc American, 284(5):34–43.

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.

(2018). Bert: Pre-training of deep bidirectional trans-

formers for language understanding. arXiv preprint

arXiv:1810.04805.

Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X.,

Celikyilmaz, A., and Weston, J. (2023). Chain-of-

veriﬁcation reduces hallucination in large language

models. arXiv preprint arXiv:2309.11495.

Funk, M., Hosemann, S., Jung, J. C., and Lutz, C. (2023).

Towards ontology construction with language models.

arXiv preprint arXiv:2309.09898.

Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y.,

Sun, J., and Wang, H. (2023). Retrieval-augmented

generation for large language models: A survey. arXiv

preprint arXiv:2312.10997.

Helskyaho, H. (2023). Towards automating database de-

signing. In 2023 34th Conference of Open Innovations

Association (FRUCT), pages 41–48. IEEE.

Kotis, K. I., Vouros, G. A., and Spiliotopoulos, D. (2020).

Ontology engineering methodologies for the evolution

of living and reused ontologies: status, trends, ﬁnd-

ings and recommendations. The Knowledge Engineer-

ing Review, 35:e4.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.,

Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov,

V. (2019). Roberta: A robustly optimized bert pre-

training approach. arXiv preprint arXiv:1907.11692.

Maran, V., Lunardi, G. M., Machado, G. M., Nasci-

mento, L., Lichtnow, D., Cezar, N. L., Gasparini, I.,

de Oliveira, E. H. T., and de Oliveira, J. P. M. (2023).

Smart campuses e ontologias: Um mapeamento sis-

tem

atico da literatura e caminhos para a pesquisa.

Anais do XXXIV Simp

osio Brasileiro de Inform

atica

na Educac¸

ao, pages 1180–1190.

Meyer, L.-P., Stadler, C., Frey, J., Radtke, N., Junghanns,

K., Meissner, R., Dziwis, G., Bulert, K., and Martin,

M. (2023). Llm-assisted knowledge graph engineer-

ing: Experiments with chatgpt. In Working conference

on Artiﬁcial Intelligence Development for a Resilient

and Sustainable Tomorrow, pages 103–115. Springer.

Mountantonakis, M. and Tzitzikas, Y. (2023). Real-

time validation of chatgpt facts using rdf knowledge

graphs. ISWC Demo Paper.

Muhamad, W., Kurniawan, N. B., Yazid, S., et al. (2017).

Smart campus features, technologies, and applica-

tions: A systematic literature review. In 2017 Interna-

tional conference on information technology systems

and innovation (ICITSI), pages 384–391. IEEE.

Neto, W. C. B. (2024). Web sem

antica e educac¸

ao dist

ancia:

Euforia, frustrac¸

ao e recomec¸o. Revista Brasileira de

Inform

atica na Educac¸

ao, 32:75–97.

Noy, N. F. and McGuinness, D. L. (2001). Ontology devel-

opment 101: A guide to creating your ﬁrst ontology.

Technical report.

Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X.

(2024). Unifying large language models and knowl-

edge graphs: A roadmap. IEEE Transactions on

Knowledge and Data Engineering.

Petroni, F., Rockt

aschel, T., Lewis, P., Bakhtin, A., Wu,

Y., Miller, A. H., and Riedel, S. (2019). Lan-

guage models as knowledge bases? arXiv preprint

arXiv:1909.01066.

Rodrigues, F. H., Lopes, A. G., dos Santos, N. O., Garcia,

L. F., Carbonera, J. L., and Abel, M. (2023). On the

use of chatgpt for classifying domain terms according

to upper ontologies. In International Conference on

Conceptual Modeling, pages 249–258. Springer.

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert,

H., Elnashar, A., Spencer-Smith, J., and Schmidt,

D. C. (2023). A prompt pattern catalog to enhance

prompt engineering with chatgpt. arXiv preprint

arXiv:2302.11382.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

104