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Knowledge representation in the ontological engineering using
conceptual modeling and graph-based reasoning
Karmen Klarin
University department of professional studies, University of Split, Split, Croatia
Stipo Čelar
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture,
University of Split, Split, Croatia
Abstract. The process of software engineering follows a few basic stages of development: the
business system analysis, the design of the new software solution, the software development with
correctness verification and, finally, the software implementation in a user environment. Ontological
engineering is also considered through the basic engineering steps. It can be perceived as a support for
the process of developing information systems through two aspects: the conceptual modeling and the
formal implementation. The conceptual knowledge is a representation of knowledge of considering the
business system domain. Tools and languages for building formal ontology support the knowledge
representation described with the conceptual model. The integration of ontological engineering in
software engineering can be considered as the improvement in the development, implementation and
use of the information system.
In this paper, the knowledge representation and reasoning formalism of ontology are presented with a
graph-based formalism. This formalism is logically founded, and it is a key feature for knowledge
representation and reasoning.
An ontology lifecycle development starts with competency questions about the domain specification.
The specification of concepts and basic relationships by a conceptual graph model are the foundations
for the modeling of formal ontology elements such as rules and constraints. Complex queries derived
from competency questions are also presented with graph-based formalism. Relations between graphs
like specializations and generalization operations, and also mapping like homomorphism, will optimize
the ontology structure. The described process of ontology development is applied to a case of education
domain modeling.
Keywords: knowledge base, knowledge reasoning, ontology, competency questions, conceptual graph
1. Introduction
Development and maintenance processes of software solution can be complemented by the
usage and integration of ontologies. The role of ontology using is manifold: the support in
modeling, the impact on the architecture of processes and data, and the implementation of
ontology as an integral part of the new software versions [1].
Using the ontology we can show the structure of any domain, organized at the level of concepts
and their relationships. In practice ontology represents a meta-model for database model of
individual software solutions. Defined standards are used to support programs with the aim of
the better communication with the environment. In this case we are talking about achieving
interoperability (especially on the Web) among software solutions [2]. We say that the ontology
allows efficiency of collecting and processing the diverse and multi-purpose knowledge. So the
ontology becomes an integral part of knowledge representation. Using the software solution
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supported by the ontology [3] and by Semantic Web [2] [4] we have the following important
features:
• Providing communication between people and systems supported by computer because it
reduces the conceptual and terminological ambiguities. The ontology increases the
consistency of information, eliminates ambiguity and combines different versions of the
same domain.
• Information retrieval on the Internet is facilitated by using the ontology. The ontology
contains information structured in a vocabulary. Web becomes a huge dictionary that
provides faster and more comprehensive access to the source of the requested
information.
• Accessing to information from the user or the system can be expressed in an unfamiliar
language or an unknown format. Ontology helps to identify information and helps to
improve associations between sets of concepts and relations between concepts.
• Interoperability as an interaction among different users or software tools for data
exchange. In this case the ontology plays a role of the reference domain model that will
be able to support translation from one programming language to another and from one
data structure to another.
A development and implementation of the ontology is not an easy task. This problem requires
a detailed and sophisticated development methodology, and even with this strategy ontology
development is more an art than conventional technological or engineering task [4].
Authors of ontology development use ontological editors that offer a graphical interface for
creating and editing ontology which is necessary to define the ontological concepts, their
attributes, properties and relationships. After this activity it is necessary to decide how to record
the ontology in a formal language such as OWL and RDF / RDFS [5].
In this paper we proposed a method of ontology development combining the best practices of
several established sources. We are using, so called, competency questions [6] in learning about
domain (such as user requirements in software engineering). Elements of ontology modeling
are creating according to the ontology engineering such as methodology in [5]. For the domain
knowledge representation and reasoning we are using techniques of conceptual graphs [7]. This
procedure follows the development of a case study of the Human Resource Management
(HRM) domain.
The second chapter of this paper describes opportunities provided by ontological engineering
for upgrading the software solution development and an information system architecture. The
third chapter defines specification purpose and scope for example of ontology cluster for HRM.
We create a part of concepts and relations taxonomies for HRM ontologies. In the fourth chapter
we have used, so-called, Competency Questions (CQs) for developing an ontology purpose and
scope. For those CQs examples we made compatible conceptual graphs. The fifth chapter is
composed of (1) an example of the conceptual graph formalized into RDF(S) language, and (2)
the conceptual graphs homomorphism that help us to demonstrate that individual programs can
be upgraded with ontology. The conclusion highlights the possibility of sharing the software
engineering and the ontological engineering.
2. Ontologies in software engineering
The software (or the information system) construction is a complex process, and it is necessary
to look at the overall architecture of the business processes. The study of enterprise business
includes activities like modeling the business organization, the business information and the
business technology. These models are then used in business software engineering, from
problem analysis, program's design, program's development, to testing and implementation in
user's environment.
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2.1 Ontological engineering as an extension of software engineering
Software engineering is composed of several basic stages of development such as analysis of
the business system, the design of future software, development of the software solution,
testing, validation and verification, and, finally, the implementation in the user environment
(Figure 1). At the very beginning, before development activities, it is necessary to make a
project plan and also, consider the business problems and the scope of the required solutions. It
is necessary to create a schedule of software development activities according to the selected
methodology.
The complexity of development of the entire information system required to pay attention to
the information system architecture [8] [9] [10]. Except the functionality of business it is
necessary to examine the relationships of different business subsystems, especially data and
information sharing among these subsystems. These requirements have influence to the
architecture of databases, software, hardware, communication and so on.
Figure 1 Software development and Ontology development
On the other hand, methodologies for ontology development are used in various areas of
business and human activities [4] [5] [11]. To improve the process of information system
development and quality assurance of software solutions we are considering the influence of
ontological engineering as a part of software engineering.
In the ontology development we also recognize the basic engineering steps. Figure 1 shows the
ontological engineering composed of the specification of domain models, the conceptualization
of domain elements and the formalization of these elements in the computer supported
ontology. We can conclude that the combination of the ontological engineering and the software
engineering together is an improvement of development, implementation and use of the system
[12].
2.2 Ontological engineering process
The elaboration of basic stages of the ontology development is described in detail in several
methodologies [4] [5] [11] [12]. Some methodologies describe only the ontology development
and some others are comprehensive and include planning, management of ontology developing
and results monitoring. Examples of such methodologies are METHONTOLOGY [5] and
NeOn [13].
According to aforementioned references, we can conclude that, in addition to the basic stages
of ontology development, we need a detailed specification of each phase (Figure 2). Like a
classical requirements engineering in the software engineering process, the ontology
development starts with the domain specification which is composed of (1) the specification of
purpose and usage of the ontology, scope of the domain and, finally, degree of the formality,
and (2) data collection using different methods. The conceptualization phase is divided into (1)
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the conceptualization of domain vocabulary and the result is the preliminary ontology, and (2)
consideration of possible integration with other ontologies. The implementation phase is
composed of (1) the formalization in an ontology language and (2) the evaluation of the
completeness, consistency and redundancy of developed ontology. Most of these steps, we will
describe and use in modeling the HRM case study.
Figure 2 Ontological engineering phases and models
Figure 2 shows the steps of the ontology development and input/output documents or models.
Techniques used in this paper for modeling ontology elements during ontological engineering
are motivating scenarios and competency questions for the domain description [6], and graph-
based knowledge representation and reasoning for the ontology structure [14]. Logical
sentences are a form of knowledge representation described by CQs. Logical sentences are
written using conceptual graphs. Construction, testing and modification of conceptual graphs
are accompanied by reasoning formalisms.
3. Ontology design for the domain of Human Resource Management
A HRM problem will be the basis for a case study where we will present (1) development of
ontology model elaborated towards activities in Figure 2, (2) model of domain knowledge using
conceptual graphs, and (3) application in a Web environment by testing the possibility of
matching a one software example in the default ontological model.
Literature [15] [16] describes mentioned problem and suggests some solutions. The enterprise
management emphasizes the HRM as an ongoing process that important fields are shown in
Figure 3. The focus of the case study will be on education, competence and knowledge.
3.1 Ontologies cluster for Human Resource Management
The set (the cluster) of HRM ontologies are based on two key elements: Job Offer Ontology
and Job Seeker Ontology. Job Seeker Ontology includes information from a CV like education
and acquired knowledge, previous jobs, skills and competences. Job Offer Ontology contains
information about employers, job offers and job vacancies.
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Education Ontology includes information about levels and fields of education. Fields of
education are based on FOET1 taxonomy and level of education on standard ISCED 972 [17].
Figure 3 Ontologies cluster for Human Resource Management [18]
Occupation Ontology includes information about occupations and workplaces. An integral part
of this ontology is taxonomy of workplaces standard ISCO-883 [19]. In the following discussion
it will be analyzed the set of occupations associated with Job Seeker Ontology work experience
and workplace, or required work experience from Job Offer Ontology.
Compensation Ontology describes the concepts related to salaries and wages for employees. An
integral part of this ontology is the currencies concept standard ISO 42174. Economic Activity
Ontology describes a model of economic activities which includes a taxonomy of standard
NACE5 [20]. For simplicity and because of the case study specification of purpose,
Compensation Ontology and Economic Activity Ontology will not be include in further
considerations.
Competency Ontology combines several sub-ontologies from different fields of competences.
Figure 3 shows examples for skills, language and driving license. Competency Ontology and
Job Offer Ontology are connected via the vacant jobs. Both are related to Job Seeker Ontology
through the application of candidates. Education Ontology is connected via competencies
required by education. For simplicity of further analysis of our case study, only Competency
Ontology (not its sub-ontology) will be considering. It is not easy to give a precise definition of
competence. Many papers highlight this issue and give a proposal of definition. So the
1 FOET Fields of Education and Training 2 ISCED International Standard Classification of Education was adopted by the UNESCO General Conference in
November 2011. The ISCED classification serves as an instrument to compile and present education statistics both
nationally and internationally. The framework is occasionally updated in order to better capture new developments in
education systems worldwide. 3 ISCO-88 International Standard Classification of Occupations 4 ISO 4217 International Standard for currency codes 5 NACE European Classification of Economic Activities
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definition from the paper ECTS User's Guide [21] says that the competencies include dynamic
combination of knowledge and understanding, intellectual and practical skills, and so on.
Clusters described above are extended with Knowledge Ontology. Concepts of knowledge areas
are associated with Education Ontology and Competency Ontology. For our case study, we have
set a small part of the Computer science knowledge area [22].
3.2 The importance of the selected ontologies cluster and fraction of their taxonomy
We have seen that ontologies clusters described above (cluster of Job Offer Ontology and
cluster of Job Seeker Ontology) containing well-defined standards. Concepts and relations of
standardized taxonomies are the foundation for automating queries addressed to the potentially
interested actors. For example, after RH joined EU, our citizens have the opportunity for
employment in the large labor market. However, if we have search (via the Web) these wide
area, we probably will not receive clear answers about job offer or job seeker and what are their
requests. So far we have looking for the job in RH where we know a lot of information about
enterprises, their activities, workplaces and so on. Now, we can access to the large number of
data at EU level, but these data are not unified in one location neither are grouped by businesses,
professions, education areas or languages. Therefore, it makes sense to modeling the conceptual
knowledge level about this complex issue, and for the beginning it is exactly the model in Figure
3. The problem is very complex and detailed design of all clusters and related ontology exceed
the scope of this paper (there are examples of case studies that deal with these details, such as
[15] [18]).
However, for further analysis of our case study we should develop our ontologies in more
detailed elements (concepts and relations). Figure 4 shows only the part of concepts taxonomy
and relations taxonomy. We will specify the definition of vocabulary in the following way:
Definition of vocabulary: Let a triple (𝑇𝐶 , 𝑇𝑅 , 𝐼) is consists of 𝑇𝐶, 𝑇𝑅 and 𝐼 that are finite pairwise
disjoint sets. The set of concepts 𝑇𝐶, the set of relations 𝑇𝑅, and the set of individuals 𝐼 defined
vocabulary 𝒱 = (𝑇𝐶 , 𝑇𝑅, 𝐼), and there are satisfying the following conditions:
• 𝑇𝐶 is the set of concepts with a subsumption6 relation, denoted ≼, and with maximal element
denoted ⊤,
• 𝑇𝑅 is the set of relation divided into subsets 𝑇𝑅1, … , 𝑇𝑅
𝑘 with arity 1,… , 𝑘, respectively. Any
two relations with different arities are not comparable. Every relation subset has subsumption
hierarchy.
By the definition, in Figure 4 is shown the part of the vocabulary of the HRM. There is a set of
concepts (A), a set of relations (B) and an example of individuals (C). An example of the
ontologies cluster is made in the tool CoGui [23].
4. Ontology specification and conceptualization
Specification of the problem begins with the purpose of our case study.
The first step in the specification of ontology is the creation of, so-called, motivating scenarios,
which describe a business problem. Scenarios are the basis for defining the competency
questions about a detail level of conceptualization. Furthermore, these questions are the basis
for the development of ontology elements, like dictionaries and taxonomies, and also rules
among concepts and relations [24].
6 A subsumptive containment hierarchy is a classification of object classes from the general to the specific. Other names
for this type of hierarchy are taxonomic hierarchy and is-a hierarchy. The taxonomical structure is a subsumptive
containment hierarchy.
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Figure 4 A part of ontology cluster for Human Resource Management
4.1 Motivating scenarios
We chose one motivating scenario that describes an example of request for workplace:
In the job offer of ICT sector someone is seeking candidates for Computer Systems
Designers/Analysts/Programmers jobs, for example, for the working place of BI/DWH7
developers. Required knowledge must be in the field of Software Engineering (specifically in
fields of Software Design and Construction Software), Programming (specifically in fields of
Object Oriented Programming and Algorithms and Complexity) and Data Management and
Databases (specifically in fields of Database Structure, Query Languages and Data
Warehousing and Data Mining). Candidates must have a degree of bachelor or master of
profession of ICT studies. The skills that candidates must have are excellent knowledge of
software tools such as Word, Excel, PowerPoint, JavaScript, C # and SQL Server or Oracle.
7 Business Intelligence / Data Warehouse
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This scenario describes a typical example of job advertising. This is a good reason to modeling
the scenario functionality through the ontologies cluster, like the example in Figure 3.
According to [6] [25] the detail specification of the scope and usage will be written in the form
of competency questions.
4.2 Competency questions and conceptual graph
Competency questions (CQs) are written in the informal form and they describe elements of
ontology. Responding to these questions ontologist checks taxonomy of concepts and relations,
and develops other structure of the ontology. CQs are made on the basis of motivating scenarios.
For the scenario from the previous chapter, here are some important CQs:
CQ1) Job Offer provides jobs for a certain profession and completed education with specific skills
and competencies.
CQ2) Job Seeker was completed ICT education, he has a knowledge about databases and
programming, and he is looking for developer jobs.
CQ3) The person was working as a software engineer and acquired 3th8 level of skills, and also,
he was working as a programmer and acquired 4th9 level of skills.
CQ4) What skills can expect employers for completed education in the field of software and
application analysis/development?
In the practice, the use of CQs is quite subjective and depends on the ontologist experience
(after all, this process is similar to the user requiremets specification in software engineering).
One of the important dilemma when we are creating the vocabulary and taxonomies is an
identification of granularity. It is important, but not easy, to determine the level of detail when
we build the structure of concepts (also the structure of relations). For example, in Figure 3, we
can ask the question whether we will develop Education Ontology on the level of overall FOET
taxonomy, or we will make only the elementary level. Because of the limited space of this paper
an example of taxonomy, Figure 4 shows a small part of whole ontologies cluster.
Four examples of CQs will be written in the form of graph-based sentences. Parts of these
sentences are concepts and relations with each other graphically related. We will specify the
definition of conceptual graph in the following way:
Definition of Conceptual graph: A basic conceptual graph over a vocabulary 𝒱 = (𝑇𝐶 , 𝑇𝑅 , 𝐼), is a
4-tuple 𝐺 = (𝐶, 𝑅, 𝐸, 𝑙) satisfying the following conditions:
• A triple (𝐶, 𝑅, 𝐸) is a finite bipartite multigraph. 𝐶 is the set of concept nodes, 𝑅 is the set of
relation nodes, and 𝐸 is the family of edges,
• 𝑙 is a labeling function that joined nodes and edges,
• Edges incident to a relation node 𝑟 ∈ 𝑅 are totally ordered and they are labeled from 1 to arity
of relation 𝑟.
According to [26] each recognized CQs will be modeled in appropriate conceptual graph. All
relations in the conceptual graphs in Figure 5 have arity of relation 2. Labels of concepts and
relations are specified in all graphs, and the edges of relations should not be labeled because
each of edges is the first (start arrow) or the second (end arrow).
The conceptual graph in Figure 7a) is the result of procedures like generalization and
specialization of graphical structures in Figure 5 (procedures are described in [14]). If we apply
these procedures, we can define a procedure that optimized the development of the integrated
graph. In this paper we assume the existence of the graph in Figure 6. The procedure itself will
be the subject of the further research.
8 According to the ISCO-88 specification that may include skills such as communication with customer, understanding the needs of business system, and the coordination and control of the software development process. 9 According to the ISCO-88 specification that may include skills such as passed advanced courses of programming
languages, analysis of complex algorithms, design of demanding interfaces, and integration of software solutions.
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Figure 5 CQs in the form of conceptual graphs
5. Formalization and evaluation
Characteristics of the Web are the basis for the development of ontology languages (Web-based
ontology language or ontology markup language). The syntax of ontology languages is based
on HTML and XML languages. RDF (Resource Description Framework) and its extension
RDFS (RDF Schema), together denotes as RDF(S), allowing the construction of semantic
annotations given by a set of triples (subject, predicate, object) [4]. RDF(S) is a language
designed for processing metadata to support the exchange of information by the Web
(interoperability among applications).
5.1 An example of general concepts formalization
Ontologies cluster for our case study is a reference model for the structure, format and the
understanding of information. Take a look at descriptions of some basic elements of RDF(S)
language [5]. The most general class is rdfs:Resource for defining any Web resource. The class
rdfs:Class defines the class of all classes. The class rdf:Property defines the class of properties.
Some of core properties are: rdf:type states that a resource is an instance of a class,
rdfs:subClassOf and rdfs:subPropertyOf are used to define class taxonomies and property
taxonomies respectively. The conceptual graph and RDF structure share very similar
characteristics. Therefore, the conceptual graph can be written in RDF(S) language [27]
following the comparison shown in Figure 6.
Figure 6 Comparison between conceptual graph and RDF(S) triples (a part of)
Applying the aforementioned rules conceptual graph in Figure 7a) can be written in RDF(S)
triples form as it is written in Figure 7b).
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Figure 7 a) General concepts of ontology cluster for HRM and b) their equivalent in RDF(S) triples
5.2 Database and graph homomorphism
This previously described procedure makes sense if the wide community adopts the significance
of ontology concept, and also be able to involve actively in the development of ontology
conceptual structure and its implementation. Despite extensive analysis and study of possible
applications of ontology, today it is still not represented in significant extent.
By now, we was developed (a part of) the formal HRM cluster ontology. In practice, an
individual instance of HRM database can be partially mapped with the ontology through the
structure and rules. Figure 8 shows the comparison of two graphs. On the left side is an
imaginary graph-based data model of enterprise HRM business process, and on the right side
is our HRM ontologies cluster. Comparison can be done through the formal verification of
graphs homomorphism.
Figure 8 a) Graph-based data model, b) graph-based reference ontological model, and c) properties, facts and
rules for generalization/specialization
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Definition of Graph homomorphism: Let 𝐺 = (𝐶𝐺 , 𝑅𝐺 , 𝐸, 𝑙𝐺) and 𝐻 = (𝐶𝐻, 𝑅𝐻 , 𝐸, 𝑙𝐻) be two
conceptual graphs defined over the same vocabulary. A homomorphism π form 𝐺 to 𝐻 is a mapping
form 𝐶𝐺 to 𝐶𝐻 and from 𝑅𝐺 uto 𝑅𝐻 which preserves edges and related labels of concepts and
relations, and satisfying the following conditions:
• ∀(𝑟, 𝑐) ∈ 𝐺 ⇒ (𝜋(𝑟), 𝜋(𝑐)) ∈ 𝐻, where is 𝑟 ∈ 𝑅𝐺 and 𝑐 ∈ 𝐶𝐺, and
• ∀𝑒 ∈ 𝐶𝐺 ∪ 𝑅𝐺 ⇒ 𝑙𝐻(𝜋(𝑒)) ≼ 𝑙𝐺(𝑒).
According to the definition and using the rules listed in the table in Figure 8c) we can conclude
that the left graph, Figure 8a), is homomorphic with the right graph, Figure 8b). This example
shows how the software solution can adapt and upgrade to the existing ontology.
6. Conclusion
A practical application of the ontology today is still lagging behind the potential that it offers.
Two problems are constantly present. The first is the development of an ontology for some
domain because it is a tedious task for the whole community of interest. The second problem is
the inclusion of individual software solutions in developed and usable ontology. Adjustment
software solutions requires an extra effort that may not be cost effective or may not be known
for developers.
However, the Internet (specifically Semantic Web), business expansion and globalization
emphasize the importance of the software solution customization with the ontological platform.
According to the stages of ontology development, the ontology specification and
conceptualization can extend the process of software solution reengineering. Comparing
conceptual structures using mapping such as homomorphism helps to identify the place where
one can customize and change the existing software solution.
In the further work it is necessary to develop the comparison process for the structure of
individual software solution, comparing it with the existing ontology. This process is time
consuming because it involves all phases of the ontology development. Therefore, it is
important to improve the activities of the ontology development which can enhance the
software solution.
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