=Paper=
{{Paper
|id=Vol-3905/master2
|storemode=property
|title=Using competency questions to enhance patterns selection in ontology patterns
languages
|pdfUrl=https://ceur-ws.org/Vol-3905/master2.pdf
|volume=Vol-3905
|authors=Glaice Kelly Monfardini,Monalessa Barcellos
|dblpUrl=https://dblp.org/rec/conf/ontobras/MonfardiniB24
}}
==Using competency questions to enhance patterns selection in ontology patterns
languages==
https://ceur-ws.org/Vol-3905/master2.pdf
Using competency questions to enhance patterns selection
in ontology patterns languages
Glaice Kelly da Silva Quirino Monfardini1,2,* , Monalessa P. Barcellos1
1
Ontology & Conceptual Modeling Research Group (NEMO), Computer Science Department, Federal University of Espírito Santo,
Vitória - ES, Brazil
2
Federal Institute of Espírito Santo, Aracruz - ES, Brazil
Abstract
Developing ontologies is a complex task and reuse can be helpful. A recent promising reuse approach is the
use of Ontology Pattern Languages (OPLs). An OPL establishes relationships between ontology patterns (OP)
and provides a process guiding the selection and use of them for modeling problem-solving. By following the
OPL flow, the ontology engineer can select the desired OPs and reuse them. However, some limitations have
been noticed when using OPLs, particularly regarding the pattern selection mechanism. Aiming to improve
pattern selection in OPLs and strengthen their use, in this work, we investigate the use of Competency Questions
(CQs) to support the selection of OPs in OPLs. CQs play an important role in the ontology development lifecycle,
as they represent the ontology requirements. They are questions that the ontology should be able to answer.
With them, it is possible to have a notion of which are the relevant concepts of the domain and the relationships
between them. Therefore, we hypothesize that using CQs in OPLs helps ontology engineers select suitable OPs
to be reused in the development of new ontologies.
Keywords
Reuse, Competency Questions, Ontology Patterns Language, Ontology Engineering
1. Introduction
Although nowadays ontology engineers are supported by a wide range of ontology engineering methods
and tools, building ontologies is still a difficult task even for experts [1]. Reusing knowledge resources,
specifically ontology patterns (OPs), has become a popular technique in the Ontology Engineering field.
Ontology reuse allows for speeding up the ontology development process, saving time and money, and
promoting the application of good practices [2]. A proposal that shows promise in this context is the use
of Ontology Pattern Language (OPL). OPLs favor reuse through their network of interconnected Domain-
related Ontology Patterns (DROPs) that provide holistic support for the development of ontologies in a
given domain [3].
An OPL contains a set of interconnected DROPs, as well as a modeling flow that provides guidance
on how to use and combine them in a specific order to solve some modeling problems in that domain [4].
The notion of OPL provides a strong sense of connection between DROPs, expressing different types of
relationships between them [4]. For example, to be applied, a pattern may require the prior application
of other patterns (dependency); a larger pattern can be composed of smaller ones; or multiple patterns
may solve the same problem in different ways (variant patterns). These relationships impose restrictions
on the order in which patterns can be applied. Thus, an OPL provides explicit guidance on how to use
and integrate related patterns into a concrete conceptual model of a new domain ontology [3]. OPLs
encourage the application of one pattern at a time, following the order prescribed by the language. By
allowing the selection of DROPs in a guided manner, an OPL frees the ontology engineer from the need
to know in advance the entire set of concepts and relationships covered by the OPL [3].
To develop a domain ontology using an OPL, the ontology engineer must select the patterns that
Proceedings of the 17th Seminar on Ontology Research in Brazil (ONTOBRAS 2024) and 8th Doctoral and Masters Consortium on
Ontologies (WTDO 2024), Vitória, Brazil, October 07-10, 2024
*
Corresponding author.
$ glaice.monfardini@ifes.edu.br (G. K. d. S. Q. Monfardini); monalessa@inf.ufes.br (M. P. Barcellos)
0000-0002-1559-6201 (G. K. d. S. Q. Monfardini); 0000-0002-6225-9478 (M. P. Barcellos)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
are pertinent to their domain of interest. Then, the ontology engineer can complement the conceptual
model resulting from the integration of the selected patterns to address aspects not covered by them.
The ontology engineer can, for example, create new concepts as specializations of concepts from the
selected patterns to address specific aspects of the domain or include new concepts and relate them in
another way (for example, through associations) to the concepts arising from the patterns.
For effective reuse of ontology patterns in OPLs, two main functionalities must be guaranteed:
selection and application. The selection corresponds to obtaining the most appropriate pattern for the
domain modeling problem to be addressed [5]. Aiming to address these functionalities, in [6], OPL-ML,
a modeling language for representing OPLs, was proposed. When using OPLs represented with this
modeling language, the ontology engineer must follow the flows of a process model (an adaptation of
the UML activity diagram) and apply one pattern at a time, according to the order of the patterns in
the paths chosen in the model. Thus, the selection is supported by the OPL process model that guides
the ontology engineer in choosing the patterns necessary for modeling the domain of interest. The
application, in turn, is assisted by two models (process model and structural model) plus the patterns
specification [3].
In recent years, OPLs have been developed for various domains, such as software processes [4, 7],
organizations [8], measurement [9] and services [10]. Even when using OPL-ML Quirino et al. [6],
experiences of applying these OPLs revealed a great effort required to use the OPL specification that, to
date, is recorded in documents. Following the flow in the process model of a large OPL can be confusing
and obtaining the desired patterns only in document format requires the effort of manually creating
the conceptual model of the selected patterns in a modeling tool. Moreover, following the process
model to select the patterns may hinder details about the patterns scope because the model does not
provide much information (e.g., to avoid being visually polluted). Therefore, although OPLs speed up
the ontology development process, there is still a need to improve pattern selection to enhance the
support OPLs provide to the ontology development process.
Competency Questions (CQs) have been recognized as a useful method for representing ontology
scope and identifying the questions that the ontology should be able to answer. CQs are thus a means
to determine ontology specifications to design and evaluate a knowledge model [11]. Considering
the potential of CQs in the development of ontologies, particularly in representing requirements, we
hypothesize that using CQs helps ontology engineers select suitable ontology patterns of OPLs to be
reused in the development of new ontologies. Thus, the doctoral research described in this paper aims
to explore the use of CQs to support the selection of patterns in OPLs and answer the following research
question: How can CQs be used to enhance pattern selection in OPLs? As a contribution to the state
of the art, this work will provide a multivocal panorama of the use of CQs in Ontology Engineering,
promoting a better understanding of the use of this method considering the literature records and also
ontology engineers reports. Additionally, we will build knowledge and theoretical support (guidelines)
on the use of CQs to structure OPLs using CQs to facilitate their understanding and support pattern
selection. As a contribution to the state of the practice, we will restructure existing OPL taking CQs as
a basis and we will develop computational support to help ontology engineers use OPLs by decreasing
the complexity of their specification.
This paper is organized as follows: Section 2 presents some background notions that support this
proposal, Section 3 discusses some related work, Section 4 briefly describes the research method, and
Section 5 presents the current state of research and outlines future work.
2. Background
Ontology reuse has long been recognized as a key attribute of ontologies, but the principles and practices
of reuse remain underdeveloped. The lack of design through reuse presents a serious problem for the
ontology community. In general, reuse can be defined as the process in which available ontological
knowledge is used as input to generate new ontologies [12]. It is a special case of design, referring
to the task of taking some existing ontology and manipulating it in some way to satisfy the design
requirements. Some more specific, related, and sometimes overlapping subtypes of reuse have been
defined, such as merging and alignment, integration, and the application of OPs [13].
The use of OPs is an approach that favors the reuse of encoded experiences and good practices [4].
Patterns are vehicles for encapsulating knowledge. A pattern describes a recurring problem that arises
in specific contexts and presents a well-proven solution for the problem [14]. Domain-related OPs
(DROPs) are reusable fragments extracted from domain ontologies. DROPs should capture the core
knowledge related to a domain, and thus they can be seen as fragments of a core ontology of that domain
[15]. A domain ontology typically results from the composition of several DROPs, with appropriate
dependencies between them, plus the necessary expansion based on specific needs [16]. DROPs are
often organized in catalogs. However, this is not enough to truly favor reuse. In a conventional catalog,
there is a lack of a strong sense of connection. We need something stronger than simply knowing that
another pattern in the collection is related in some way. When collections are presented in conjunction
with, e.g., pattern sequences, we get a stronger sense of connection [14]. This is especially important
for reusing DROPs. Thus, a good choice is to organize DROPs in OPLs [4].
An OPL is a network of interrelated DROPs that provides holistic support for solving ontology
development problems for a specific domain. An OPL contains a set of interrelated DROPs plus a
process providing explicit guidance on what problems can arise in that domain, informing the order
to address these problems, and suggesting one or more patterns to solve each specific problem. An
OPL is not a method for building ontologies. It only deals with reuse in ontology development, and its
guidance can be followed by ontology engineers using whatever ontology development method that
considers ontology reuse as one of its activities. Some examples of OPLs can be found at [17].
In Ontology Engineering, CQs play a paramount role. They represent the requirements of the
ontology and are an effective way to determine what is relevant to the ontology and what is not. CQs
can be used to support both, ontology specification and ontology evaluation [18]. In the former, CQs
help model the domain, i.e., through questions that the ontology should be able to answer, it is possible
to have a notion of which are the relevant concepts of the domain and the relationships between them.
In the latter, CQs can be used to identify ontology flaws in domain modeling, and thus contribute to the
ontology quality assessment [19].
3. Related works
Exploring CQs in ontology development is not a new idea in itself [19, 20, 21]. CQs have been used
mainly in requirements specification and ontology evaluation and, thus, help define the ontology scope
and evaluate the ontology conceptualization [22, 23, 18, 24, 25, 26]. However, CQs have also been used
to assist in other ontology development contexts. For example, some works have proposed approaches
that use CQs to generate ontologies automatically or semi-automatically. In [27] was presented the
Frame-based Ontology Design Outlet (FrODO), which is a method and tool capable of automatically
constructing ontologies from CQs. The domain ontologies produced by FrODO are drafts that can be
used to feed agile ontology design methodologies. In [28] a new method for developing ontologies
that exploit analogies between different but related knowledge domains represented by ontologies was
proposed. It processes two ontologies for related problems or knowledge domains, where one of them is
complete while the other needs further development; then, it generates a series of CQs for the domain
expert to extend the new ontology that will be generated as a result.
There are also some works in the literature that propose the use of CQs in the selection of DROPs.
In [29], the author developed a DROPs search method exploiting both the similarity between pattern
CQs and user queries, and the relative abstraction level of general pattern solutions versus concrete
user queries in XD Tools, a method shown to increase recall when searching for candidate DROPs. [30],
in turn, describes an approach, along with an evaluation strategy for a tool named ODPReco that can
recommend the possible DROPs to use in a given ontology. ODPReco analyzes the lexical, structural,
and behavioral aspects of an ontology, along with learning from existing DROP implementations to
recommend DROPs that can be used for refactoring an ontology. The behavioral aspect of the ontology
can be analyzed by making use of the CQs associated with the given ontology. However, they highlight
the absence of CQs in some ontologies, which impedes this behavioral analysis. [31, 32] proposed
strategies for selecting DROPs to improve the quality of existing ontologies. Among the strategies,
they suggested the selection of patterns via requirements (using the CQs of the DROPs). Then, they
compared the CQs of the DROPs base with the CQs of the ontology to be improved. They concluded
that this strategy was hampered by the fact that CQs do not need to completely cover the scope of
the ontology and, mainly, because of the difference in the level of abstraction between them, which
varies according to the ontology engineer. The need to improve the documentation of DROPs was also
evident.
Like in the aforementioned works, in our work, we propose to use CQs in the selection of patterns.
However, this selection occurs in the context of OPLs. Unlike pattern catalogs, OPLs include, in addition
to the patterns themselves, a rich description of their relationships and a process that guides the order
of application according to the problems to be modeled. OPLs patterns come from a core ontology and
have a documentation standard that facilitates the use of these patterns. This solves the problems of
abstraction levels, scope and documentation mentioned in the works above. By using CQs, we intend
to improve the pattern selection mechanism in OPLs, facilitating their understanding and application.
4. Research method
The research approach adopted in this work is exploratory empirical with studies involving both
quantitative and qualitative approaches. We follow the Design Science Research (DSR) paradigm, which
concerns extending “human and organizational capabilities by creating new and innovative artifacts”
[33]. In this work, the main artifact to be produced is a set of guidelines on the use of CQs to structure
OPLs. From this, others will be built: OPLs restructured taking CQs as a basis and (ii) a supporting
tool to OPL use. DSR comprises the following steps [34]: (i) Problem identification and Motivation, (ii)
Definition of the objectives for a solution, (iii) Design and Development, (iv) Demonstration, (v) Evaluation,
and (vi) Communication. These steps are organized in an iterative process, with three cycles: Relevance
Cycle, Design Cycle, and Rigor Cycle.
A DSR project begins with the Relevance Cycle, which involves defining the problem to be addressed,
the requirements, and the criteria for evaluating the results [33], including the steps (i) and (ii). The
Design Cycle involves developing and evaluating artifacts or theories to solve the identified problem [33],
comprising steps (iii), (iv), and (v). Finally, the Rigor Cycle refers to using and generating knowledge
[33], comprising the step (vi) and the use of knowledge and foundations along with the work. These
steps, the results produced so far, and future actions are described in the next section.
5. Current state of the research and future works
In the Problem identification and motivation step, the problem was identified from the literature (e.g.
[6]) and it was also observed in practice by some ontology engineers when applying OPLs. In the
current approach, which uses OPL-ML [6] and records the OPL specification in documents, the ontology
engineering needs to follow the flows of the OPL process model to select the desired patterns. In
the model (adapted from the UML activity diagram), the patterns are represented as rectangles with
the pattern name inside it and arrows are used to represent the OPL flow by connecting the patterns
according to their relationships (e.g., dependency, variance). To avoid being visually cluttered, the
process model does not provide much information about the patterns or conditions to select them. Thus,
the ontology engineering often needs to look for information in the OPL specification, making the OPLs
use tiresome and confusing, particularly for large OPLs. Moreover, while following the OPL process
frees the ontology engineering of deep knowledge about the domain, it also makes pattern selection
less flexible and dos not stimulate the ontology engineer to reflect about the ontology scope in-depth.
These difficulties were initially reported by OPL users in seminars and other meetings discussing the
topic. Therefore, it was noted that although OPLs speed up the ontology development process, there
is still a need to improve pattern selection to enhance the support that OPLs provide to the ontology
development process. Given how much CQs help in defining the scope of patterns and understanding
them, we envisioned the possibility of using the CQs to improve patterns selecion.
Thus, in the Definition of the objectives for a solution step, we decided to explore the use of CQs to
support the selection of patterns in OPLs and provide theoretical (guidelines) on the use of CQs to
structure OPLs and computational (tool) to support using CQs to select patterns in some OPLs.
In the Design and development step, we aim at developing the guidelines. We started by performing
two studies to acquire knowledge about CQs. The first study was a survey with 63 ontology engineers
with experience in CQs ([35]). The survey provided a panoramic picture of how CQs have been used in
ontology engineering. In summary, the results showed that CQs have been considered useful and have
helped mainly define ontology scope and evaluate ontology conceptualization. However, although there
are ontology engineering methods that provide guidelines to define CQs, they are still limited, which
causes ontology engineers to face difficulties when writing, using, and managing CQs. The second
study is a systematic literature mapping [36] to give us a panorama of the state of the art about the
use of CQs in Ontology Engineering in the requirements specification context. The study follow the
guidelines defined in [36, 37]. For that, we defined fourteen research questions and selected publications
in four steps. First, the search string was applied in the search mechanism of Scopus, Science Direct,
ACM, IEEE, Engineering Village, and Web of Science. After that, duplicated publications were removed.
Then, we applied the first filter by reading the abstracts of the selected publications and selecting only
publications that meet inclusion and exclusion criteria. Finally, we applied the second filter, by reading
the full texts of the publications included after the first filter and selecting only publications that meet
the criteria. As inclusion criteria, we considered that the publication must address CQs in the ontology
requirements specification context. As exclusion criteria, we considered: the publication does not have
an abstract; the publication is only an abstract; the publication is a secondary study, a tertiary study, a
summary, an editorial or a tutorial; the publication is not written in English; the publication is a copy
or an older version of an already selected publication; and the publication full text was not available.
The publications returned in the publication selection steps were cataloged and stored in spreadsheets.
Data from the selected publications was extracted and organized into a data extraction form oriented to
the research questions. We considered paper published until December 2023. In total, 298 publications
were analyzed. In the end, 44 publications were selected and data was extracted. We have recorded the
systematic literature mapping results in a paper to make the study available soon.
Considering the knowledge obtained so far, we have restructured an OPL, namely the Services OPL
(S-OPL) [10] 1 . This experience will result in the first version of the guidelines. However, for creating
the guidelines, we need to evaluate if the use of CQs improves pattern selection in OPLs. Thus, we
carried out a study with three expert ontology engineers who have already used OPLs represented
using OPL-ML [6] and faced problems with pattern selection. To perform the study, we developed a
tool to support pattern selection in S-OPL. The tool allows the ontology engineer to analyze the set of
CQs covered by the patterns of S-OPL and select the ones they judge necessary to the ontology they
intend to develop. According to the CQs that the ontology engineer selects, the corresponding OPL
patterns are selected. The tool verifies the selected CQs considering dependency relations between
the corresponding patterns and warns the ontology engineer if changes are needed. Having resolved
all dependencies, the tool generates the list of selected OPL patterns that can, thus, be integrated to
generate the desired model. Figure 1 shows a screenshot of the tool.
In the study, the participants used the tool to select patterns from S-OPL and develop ontologies for
two domains, considering information provided in two scenarios presented to the participants. After
using the tool and selecting the patterns, the list of patterns selected by each participant was sent to the
researchers (this paper’s authors), who produced the corresponding conceptual model integrating the
1
S-OPL addresses the central conceptualization of service phenomena. The S-OPL patterns were extracted from UFO-S, a
commitment-based reference ontology for services [38], whose conceptualization is based on the establishment and fulfillment
of commitments and claims between service participants (used to refer to both service providers and service customers)
throughout the service lifecycle. S-OPL focuses on the three main phases of the service lifecycle: service offering, service
negotiation and service delivery.
Figure 1: Screenshot of S-OPL Tool
selected patterns (the current version of the tool does not integrate the patterns conceptual models),
and sent back to the participants. The participants analyzed the resulting model and answered a
questionnaire about their experience in the study. Currently, analysis and interpretation of the collected
data are ongoing. After analyzing the results, we will document the first version of the guidelines,
considering the knowledge acquired so far. Then, we will evolve S-OPL and restructure other OPL
considering the study results and the defined guidelines and will conduct a new study with a large and
more varied sample of ontology engineers. After that, we will refine the guidelines considering the
study results.
In the Demonstration step, we will use guidelines to restructure or create an OPL and, thus, in the
Validation step, the guidelines will be applied by a third party. Finally, in the Communication step,
which occurs parallel to other steps, we will present the research results by publishing papers and the
complete research project will be documented in the doctorate thesis.
Acknowledgments
This research is supported by the Coordination for the Improvement of Higher Education Personnel -
CAPES Brazil (Finance Code 001) and the Espírito Santo Research and Innovation Support Foundation -
FAPES (Processes 2023-5L1FC, 2022-NGKM5, 2021-GL60J, and T.O. 1022/2022).
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