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(See Table of Contents below) Abstract
The Internet and other open connectivity environments create a strong demand for the sharing of data semantics. Emerging ontologies are increasingly becoming essential for computer science applications. Organizations are looking towards them as vital machine-processable semantics for many application areas. An ontology in general, is an agreed understanding (i.e. semantics) of a certain domain, axiomatized and represented formally as logical theory in a computer resource. By sharing an ontology , autonomous and distributed applications can meaningfully communicate to exchange data and make transactions interoperate independently of their internal technologies.
The main goal of this thesis is to present methodological principles for ontology engineering to guide ontology builders towards building ontologies that are both highly reusable and usable, easier to build, and smoother to maintain.
First , we investigate three foundational challenges in ontology engineering (namely, ontology reusability, ontology application-independence, and ontology evolution). Based on these challenges, we derive six ontology-engineering requirements. Fulfilling these requirements is the goal and motivation of our methodological principles.
Second , we present two methodological principles for ontology engineering: 1) ontology double articulation, and 2) ontology modularization. The double articulation principle suggests that an ontology be built as separate domain axiomatizations and application axiomatizations. While a domain axiomatization focuses on the characterization of the intended meaning (i.e. intended models) of a vocabulary at the domain level, application axiomatizations mainly focus on the usability of this vocabulary according to certain application/usability perspectives. An application axiomatization is intended to specify the legal models (a subset of the intended models) of the application(s)’ interest. The modularization principle suggests that application axiomatizations be built in a modular manner. Axiomatizations should be developed as a set of small modules and later composed to form, and be used as, one modular axiomatization. We define a composition operator for automatic module composition. It combines all axioms introduced in the composed modules.
Third , to illustrate the implementation of our methodological principles, we develop a conceptual markup language called ORM-ML, an ontology engineering tool prototype called DogmaModeler and a customer complaint ontology that serves as a real-life case study.
This research is a contribution to the DOGMA research project, which is a research framework for modeling, engineering, and deploying ontologies. In addition, we find we have benefited enormously from our participation in several European projects. It was through the CCFORM project (discussed extensively in chapter 7) that we were able to test and debug many ideas that resulted in this thesis. The Network of Excellence KnowledgeWeb has also proved to be a fruitful brainstorming environment that has undoubtedly improved the quality of the analyses performed and the results obtained.
Table of Contents
Introduction and Overview
1.1 Scope and motivation
1.1.1 Foundational challenges in ontology engineering
1.1.2 Types of methodologies
1.2 Summary of the main goals and contributions
1.3 Thesis outline and structural overview
Fundamental Challenges in Ontology Engineering
2.1 Ontology reusability
2.1.1 Significance of ontology reusability
2.1.2 Reusability challenges
2.2 Ontology application-independence
2.2.2 Related work
2.2.3 Ontology usability is also important
2.3 Ontology evolution
2.3.1 The complexity of change
2.3.2 Distributed evolution
2.3.3 Alternative axiomatizations
Ontology Double Articulation
3.1.1 Overview of the double articulation principle
3.2 Domain Axiomatization
3.2.1 Definition (double articulation, intended models, legal models)
3.2.2 Importance of linguistic terms in ontology engineering
3.2.3 On representing domain axiomatizations
3.2.4 Summary: properties of domain axiomatization
3.3 The notion of an ontology base
3.3.1 Definition (Lexon)
3.3.2 Definition (Concept)
3.3.3 Definition (Role)
3.3.4 Definition (Mapping lexons into first order logic)
3.3.5 The notion of context
3.3.7 Further formal axiomatizations (Incorporating upper level ontologies)
3.4 Application axiomatization
4.1.1 A simple example
4.2 Related work
4.3 Our approach
4.3.1 Modularity criterion
4.3.2 Module composition
4.4 Formal framework
4.4.1 Definition (Module)
4.4.2 Definition (Model, Module satisfiability)
4.4.3 Definition (Composition operator)
4.4.4 Definition (Modular axiomatization)
4.5 Composition of ORM conceptual schemes
Step 1: Composing fact types.
Step 2: Composing constraints.
Step 3: Reasoning about the satisfiability of ORM modules
4.6 Discussion and conclusions
ORM Markup Language
5.1 Introduction and motivation
5.1.1 Why ORM
5.2 ORM-Markup Language
5.2.1 ORM-ML metadata
5.2.2 ORM-ML Body
5.3 Discussion and conclusions
DogmaModeler Ontology Engineering Tool
6.1 Introduction, a quick overview of DogmaModeler
6.2 Modeling domain axiomatizations in the Ontology Base
6.2.1 Context Modeling
6.2.2 Concept Modeling
6.2.3 Lexon Modeling
6.3 Modeling application axiomatizations
6.3.1 Generating ORM-ML
6.4 Validation of application axiomatization
6.5 Axiomatization libraries
6.6 Composition of axiomatization modules
6.7 Other functionalities
6.7.1 Ontology-driven forms
6.7.2 Ontology Multilingualism
6.8 Discussion and conclusions
The CCFORM Case Study
7.2. Customer Complaint ontology
7.2.1 Customer-complaint domain axiomatization
7.2.2 Customer-complaint application axiomatization
7.4 Multilingual lexicalization of the CContology
Conclusions and Future Work
8.2 Discussion and concluding remarks
Contribution to ORM
8.3 Future Research
Appendix A: ORM Markup Language
Appendix A1 (tree view of the ORM-ML XML-Schema)
Appendix A2 (ORM-ML XML-Schema)
Appendix A3: Complete Example
Appendix B: DogmaModeler
Appendix B1: DogmaModeler Ontology Metadata
Appendix B2: XML-Schema of ORM-ML graphical style sheets
Appendix B3: ORM Verbalization Templates
Appendix C: Customer Complaint Ontology
Appendix C1: The CCglossary
Appendix C2: Lexons in the CContology
Appendix D: Thesis Glossary
The success of using ontologies to solve knowledge-related or semantic interoperability problems is related to the quality of the used ontologies. The quality of an ontology, in turn, is strongly related to the quality of the languages, methods and tools used to develop it. Thus, it is important to advance on the theoretical and practical support for ontology engineering. In this sense, we have proposed several methods, languages and tools to aid in ontology engineering.
SABiO ( S ystematic A pproach for B u i lding O ntologies)
SABiO was first proposed in 1997. It was used over the years to develop several ontologies in different domains, such as Software Engineering and Cardiology. Lessons learnt from the experiences using SABiO and the analysis of its strengths and weakness led the method to be evolved. In 2014, the current version of SABiO was published. SABiO supports ontology development by providing a set of processes and activities to be followed to produce reference ontologies (i.e., conceptual models built with the aim of describing the domain in reality, without any concern regarding computational properties) and operational ontologies (i.e., ontologies built for machine interpretation). According to SABiO, when developing an ontology, the ontology engineer should consider the Development Process plus five support processes, namely: Knowledge Acquisition, Documentation, Configuration Management, Evaluation, and Reuse. The current version of SABiO is presented in:
- FALBO, R.A.. SABiO: Systematic Approach for Building Ontologies . In: Joint Workshop ONTO.COM / ODISE on Ontologies in Conceptual Modeling and Information Systems Engineering (Co-located with 8th International Conference on Formal Ontology in Information Systems, FOIS 2014). Rio de Janeiro – RJ, Brazil. 2014.
EArly-OE ( E nterprise A rchitecture-driven ea rly O ntology E ngineering)
EArly-OE establishes strategies for the use of EA (Enterprise Architecture) models as non-ontological resources to provide knowledge in initial ontology engineering activities. It is a particularly suitable method for developing ontologies in domains rich in structured processes. EArly-OE prescribes guidelines for the use of elements of EA models to support initial activities of ontology engineering, including: identification of domain specialists and potential ontology users; selection of consolidated knowledge resources in the domain of interest; definition of the ontology intended uses; identification of the ontology scope and elicitation of functional requirements; and initial proposal for ontology modularization. Early-OE is described in:
- DETONI, A.A.. Initial Ontology Engineering Activities Supported by Enterprise Architecture Models. PhD Thesis. Post-Graduate Program in Informatics, Federal University of Espírito Santo. 2019. (In Portuguese).
CLeAR ( C onducting L iterature S e arch for A rtifact R euse)
CLeAR is a systematic approach to find and select reusable knowledge resources for building ontologies with the purpose of scientific research data integration. It follows some principles of Systematic Literature Review, supporting the search for knowledge resources in the scientific literature. CLeAR includes activities organized in three cycles. The first cycle aims at defining the data integration requirements and the scope of the ontology to be developed. The second cycle aims at systematically identifying structured resources candidates to be reused in the development of the ontology, based on the requirements defined in the first cycle. In the last cycle, structured resources are selected to be reused. CLeAR addresses specific ontology engineering activities. As a consequence, it was designed to be used as a complement to existing ontology engineering methods, such as SABiO (see above). CLeAR was proposed in:
- CAMPOS, P.M.C.. Designing a Network of Reference Ontologies for the Integration of Water Quality Data . Master Thesis. Post-Graduate Program in Informatics, Federal University of Espírito Santo. 2019.
GO-FOR ( Goal-Oriented Framework for Ontology Reuse)
GO-FOR applies GORE (Goal-Oriented Requirement Engineering) in Ontology Engineering to express the design rationale of ontology model fragments. In GO-FOR, ontology models are depicted in fragments (i.e., domain ontology patterns) related to goals. These model fragments are self-contained ontology structures called Goal-Oriented Ontology Patterns (GOOP), a new type of pattern to be applied to develop ontologies in a goal-oriented approach. In GO-FOR, goals can be used as parameters to support ontology shareability and reuse. In addition to GOOPs, GO-FOR introduces GOOPR ( GOOP Repository ), a repository to store GOOPs and that serves as an abstraction layer for ontology development. To support GO-FOR use, we developed GOOP-Hub , a tool that supports GOOPs creation, search and retrieval. An overview of GO-FOR is presented in:
- REGINATO, C. C.; SALAMON, J. S.; NOGUEIRA, G. G.; BARCELLOS, M. P.; SOUZA, V. E. S.; MONTEIRO, M. E.. GO-FOR: A Goal-Oriented Framework for Ontology Reuse . In: Proceedings of the 20th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI 2019), 2019, Los Angeles. 2019.
Guidelines on how to use GOOP-Hub are available in the GOOP-Hub User Guide .
Integra is an approach for ontology development based on integration, which uses goal modeling to support requirements elicitation activities, help make explicit design rationale and aid in the search for candidate ontologies for reuse. Integra prescribes a process composed of four phases: Ontology Requirements Elicitation, Selection of the Ontologies to be Integrated, Ontology Integration, and Evaluation of the Resulting Ontology. The main result of the application of Integra is a reference ontology (i.e., a conceptual model built with the aim of describing the domain in reality, without any concern regarding computational properties). In case there is a need for an operational ontology (i.e. an ontology built for machine interpretation), then a design phase should be executed to adjust the reference ontology for implementation. Both design and implementation are out of the scope of Integra. They are addressed in other ontology engineering methods, such as SABiO (see above). Hence, Integra may be combined with other methods for designing and implementation of operational ontologies. Integra was proposed in:
- SALAMON, J. S.. A Goal-Oriented Approach for Integration-based Ontology Development. Master Thesis. Post-Graduate Program in Informatics, Federal University of Espírito Santo. 2018. (In Portuguese).
The specification of Integra, containing a detailed description of its phases and activities, and a practical example of Integra use will be available here soon.