Ontology Development Methodology Research Articles

Unification of tsunami-related terminology: Ontology engineering perspective

Like many research areas, tsunami research has plenty of related topics that lack unified terminology. Some particular sub-topics may not have enough attention or do not share the same terminology as different views on the phenomenon. This issue can be tackled by an ontology that puts knowledge from different related topics into a formal structure that connects concepts with relationships. This paper proposes the development process of Tsunami-Related Ontology (TRO) that would aid the research in this field. The proposed semi-automatic ontology development methodology applies to any research field and does not require specific algorithms or programming skills to achieve its goal. This paper particularly focuses on three research gaps related to tsunami that are expected to benefit significantly from an ontology: meteorological tsunami, community resilience, and physical vulnerability. For these topics, the created ontology provides a formal taxonomy that links individual concepts to equivalent or related concepts, providing an easy-to-understand overview of the area.

An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.

Designing ontologies for behaviours based on temporal passive data

The use of ontologies to model human behaviours that affect health is challenging since this process involves data from multiple inter-related domains that unfold and evolve over time. However, while current ontology development methodologies are generic enough to model any domain of interest, they do not provide design guidelines for modelling time-related aspects. This paper proposes a methodology for ontology development that entails the requirements for behaviours modelling based on passive temporal data. Its main focus is on temporal representations of classes and their holistic relations since no other methodology approaches ontology design from its temporal perspective. We exemplify these ideas by modelling the sleep behaviour domain, its relations to other behavioural aspects, and its effects on health.

An ontological data model to support urban flood disaster response

This work aims to develop an ontological data model to support urban flood disaster response. The preliminary studies of the existing flood ontologies revealed that the developed ontologies do not express the relationships between the different levels of administration involved during flood disasters. So, an ontology named Flood Disaster Support Ontology (FDSO) was developed. The FDSO acts as a data model that can accommodate the various organisations and agents of different levels of the administrative agencies that are part of the flood disaster management process. It depicts their relationships and their activities specifically during an urban flood disaster response. The ontology also organises the resources and the information about them that are possessed by different organisations and may be used by first responders to carry out the activities such as rescue and relief. An amalgamation of two existing methodologies Yet Another Methodology for Ontology Development (YAMO) and NeOn was used to construct the ontology. The ontology was evaluated syntactically using reasoners along with the OOPS!, an OntOlogy pitfall scanner, whereas the semantic evaluation was done through SPARQL queries. The process yielded satisfactory results.

Methodology for agile and iterative ontology development for toolmaking

Complex manufacturing processes and the absence of repeat effects characterize the toolmaking industry. German-speaking toolmaking companies are increasingly faced with the challenge of having to reach the limits of what is technically feasible and are confronted with an erosion of know-how, induced by demographic change. This applies in particular to know-how-intensive areas such as design, CAM-programming and work preparation. There is currently no comprehensive system support in these areas and the knowledge required for planning activities is often only available in the form of implicit technical and empirical knowledge. However, the use of heterogeneous manufacturing technologies requires a profound understanding of technology along the entire value chain. As a result of the very high semantic expressiveness of ontologies, they enable the representation of the most complex data models with logical relationships that go beyond hierarchical subdivision of content. This paper presents a novel agile methodology for the development of domain-specific ontologies in the environment of toolmaking. The methodology makes it possible to integrate the implicitly existing technical and experiential knowledge of employees at an early stage in the modelling process. In particular, the developed methodology extends conventional methods by the identified deficits in terms of knowledge acquisition, iteration and agility, as well as a separate consideration of the life cycle along the ontology of the use phase, taking into account agile methods from requirements engineering. Compliance with various guidelines and requirements is mandatory, such as the formulation of competence questions and the use of a standardised specification document. The iterative approach also ensures the needs-based integration of the characteristics of toolmaking. Furthermore, the methodology enables an early integration of IT structure and user interface for the needs-based design and use of the domain-specific ontology. The validation is based on an example in the mechanical production of a toolmaking company.

New directions for applied knowledge-based AI and machine learning

In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information.

Ontology development is consensus creation, not (merely) representation

Ontology development methodologies emphasise knowledge gathering from domain experts and documentary resources, and knowledge representation using an ontology language such as OWL or FOL. However, working ontologists are often surprised by how challenging and slow it can be to develop ontologies. Here, with a particular emphasis on the sorts of ontologies that are content-heavy and intended to be shared across a community of users (reference ontologies), we propose that a significant and heretofore under-emphasised contributor of challenges during ontology development is the need to create, or bring about, consensus in the face of disagreement. For this reason reference ontology development cannot be automated, at least within the limitations of existing AI approaches. Further, for the same reason ontologists are required to have specific social-negotiating skills which are currently lacking in most technical curricula.

An Ontology Towards Predicting Terrorism Events

Although there is an increasing amount of information for counter-terrorism operations freely available online, it is a complex process to extract relevant information and to detect useful patterns in the data in order for intelligence functionaries to identify threats and to predict possible terror attacks. Automation is required for intelligent decision-making. To assist with this, in this paper, the researchers propose an ontology-based data access system for counter-terrorism. The system will enable intelligence analysts to perform specialised semantic searches about terrorist events or groups for analysis using an ontology. In this paper, the researchers present the ontology that was created by following an existing methodology for ontology development, and an ontology-based data access system together with all the components used in development (i.e., databases, web-scraper tools, ontology-based data access software, and data sources). Lastly, the ontology is demonstrated by means of use cases with example queries for generating actionable intelligence for operations.

Collaborative Decision-Making: A Proposal of an Semi-Automatic Facilitation Based on an Ontology

In this paper, we focus on a recommendation approach for collaborative decision-making. This recommendation is possible by designing an ontology in a first step. The construction of this ontology is based on a state of the art on collaborative decision-making, on ontology engineering and on collaboration engineering. An eight-step ontology development methodology was adopted and implemented to design the ontology. A corpus made up of one hundred and seventy-seven (177) documents was the starting point for the extraction of terms from the ontology and the Unified Modeling Language (UML) language is used as a description language of the ontology. This ontology is the starting point for a facilitation support system in a collaborative decision-making process. In a second step, we defined rules, based on our expertise on collaborative decision-making, used to guide a group of decision maker or a facilitator.

Ranking the ontology development methodologies using the weighted decision matrix

PurposeThe current work provides a framework for the ranking of ontology development methodologies (ODMs).Design/methodology/approachThe framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used.FindingsState-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades.Originality/valueThere is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.

Knowledge reuse for ontology modelling in Maintenance and Industrial Asset Management

Maintenance and Industrial Asset Management (AM) are fundamental business processes in guaranteeing the availability of physical assets at minimum risk and cost, while balancing the interests of several stakeholders. To reach operational excellence, intra- and inter-enterprise interoperability of systems is needed to support information management and integration between several involved parties. To this end, ontology engineering is relevant since it supports interoperability at technical and semantic levels. However, ontology modelling methodologies are varied, and several best practices exist, amongst which knowledge reuse. Nevertheless, reusing extant knowledge is not completely exploited so far, causing a heterogeneous ensemble of ontologies that are not orchestrated. The present work aims at promoting the adoption of knowledge reuse for ontology modelling in maintenance and AM. Therefore, an extensive review of existing ontologies for the two targeted business processes is performed with a twofold objective: firstly, to realise a cross-industrial ontological compendium, and secondly to understand the state of art of ontology modelling in maintenance and AM. To support the adoption of knowledge reuse, this practice is framed in AMODO (Asset Management Ontology Development methOdology). Finally, a laboratory-sized showcase is provided to prove the usefulness of relying on knowledge reuse during the ontology development. The results show that the developed ontology is realised faster and is inherently aligned with established ontologies, towards enterprise systems interoperability. Consequently, maintenance and AM business processes may rely on information management and integration to pursue operational excellence.

OnTARi: an ontology for factors influencing therapy adherence to rehabilitation

BackgroundAdherence and motivation are key factors for successful treatment of patients with chronic diseases, especially in long-term care processes like rehabilitation. However, only a few patients achieve good treatment adherence. The causes are manifold. Adherence-influencing factors vary depending on indications, therapies, and individuals. Positive and negative effects are rarely confirmed or even contradictory. An ontology seems to be convenient to represent existing knowledge in this domain and to make it available for information retrieval.MethodsFirst, a manual data extraction of current knowledge in the domain of treatment adherence in rehabilitation was conducted. Data was retrieved from various sources, including basic literature, scientific publications, and health behavior models. Second, all adherence and motivation factors identified were formalized according to the ontology development methodology METHONTOLOGY. This comprises the specification, conceptualization, formalization, and implementation of the ontology “Ontology for factors influencing therapy adherence to rehabilitation” (OnTARi) in Protégé. A taxonomy-oriented evaluation was conducted by two domain experts.ResultsOnTARi includes 281 classes implemented in ontology web language, ten object properties, 22 data properties, 1440 logical axioms, 244 individuals, and 1023 annotations. Six higher-level classes are differentiated: (1) Adherence, (2) AdherenceFactors, (3) AdherenceFactorCategory, (4) Rehabilitation, (5) RehabilitationForm, and (6) RehabilitationType. By means of the class AdherenceFactors 227 adherence factors, thereof 49 hard factors, are represented. Each factor involves a proper description, synonyms, possibly existing acronyms, and a German translation. OnTARi illustrates links between adherence factors through 160 influences-relations. Description logic queries implemented in Protégé allow multiple targeted requests, e.g., for the extraction of adherence factors in a specific rehabilitation area.ConclusionsWith OnTARi, a generic reference model was built to represent potential adherence and motivation factors and their interrelations in rehabilitation of patients with chronic diseases. In terms of information retrieval, this formalization can serve as a basis for implementation and adaptation of conventional rehabilitative measures, taking into account (patient-specific) adherence factors. OnTARi also enables the development of medical assistance systems to increase motivation and adherence in rehabilitation processes.

A systematic literature review on semantic models for IoT-enabled smart campus

Smart communities have recently gained much attention. Researchers have been trying to tackle a number of challenges faced by smart communities. Interoperability is one key challenge that occurs due to different systems using different knowledge representations. To solve interoperability problems, ontologies are seen as a promising solution as they provide a commonly agreed vocabulary for representing data that are understandable by stakeholders of smart communities. Smart communities make use of Internet of Things (IoT) and ubiquitous networks to support communication among objects and devices in such environments. Smart campuses are examples of smart communities. Recently, many articles related to ontologies focusing on smart communities and smart campuses in IoT environments, have been published. This paper presents a Systematic Literature Review that has been conducted using Google Scholar. 18 ontologies for smart communities/smart campuses have been identified and analyzed out of 341 articles from year 2010 to 2019. The review classifies the ontologies in terms of domain, ontologies being reused, availability online, limitations, language adopted and coverage. It additionally discusses on the standards, the level of expressiveness, the ontology development approaches and methodologies adopted by the identified ontologies. Our analysis shows that the identified ontologies have been developed based on different ontological commitments. None of them have come up with a core semantic model that models different collaborating domains in a smart campus such as smart learning, smart management, smart governance, smart room, smart health, smart library and smart parking among others and that enhances cross-domain interoperability in a such an environment. Further details on our findings are presented and discussed in the paper.

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however, teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multi-aspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the “e-tourism” domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a human-machine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.

An Improved Methodology for Collaborative Construction of Reusable, Localized, and Shareable Ontology

Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain. Managing knowledge in the IWs domain is complex; however, promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established. In this context, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management, better decision-making, and interoperability among diverse institutions of an IWs domain. The development of ontology involves structural and logical complexity, and requires a well-designed, mature, and widely accepted methodology, to ensure its reliability. Many methodologies for ontology development have been proposed by several researchers; however, most of the developed methodologies have not included several important phases. Furthermore, several methodologies have not provided the complete details of the techniques and activities involved in the ontology construction process. Fewer details make it difficult to follow a methodology for designing ontologies. This study aims to compare existing methodologies based on sixteen important criteria and proposes an improved methodology for ontology development for IWs domains. The proposed methodology has included several important phases such as the Estimation of Human Resources, Re-engineering and Re-using of Resources, Collaborative Ontology Construction, the Conceptualization of both Endurants and Perdurants, Localization of an Ontology, Ontology Integration and Merging, Support for Interoperability, Versioning of an Ontology, and Ontology Population. The proposed methodology has been applied to design an OntoWM domain ontology for the Waste Management (WM) domain. The evaluation of the proposed methodology has been made by utilizing the designed OntoWM domain ontology in an ontology-based web application called SmartBinAnalytics. SmartBinAnalytics has been built using OntoWM, Java Server Pages (JSP), Jena Application Programming Interface (API), and MySQL. The proposed methodology can be used for the construction of domain ontologies in any IWs domain.

Ontology-based knowledge management in income tax of Nepal

Organisational knowledge management in the government agencies is crucial to create a common understanding about data, system and procedures for stakeholders. Main purpose of this work is to create a knowledge repository for income tax of Nepal using domain ontology. To create this ontology, we have used 'Methontology' a broadly used ontology development methodology. After the knowledge acquisition; competency questions were created and conceptualisation was specified. A class hierarchy, object properties and data propertied are identified and specified. Further, ontology was created by using the Protégé tool. Consistency of the developed ontology was evaluated by correctly answering all the competency questions using SPARQL. The ontology can be used by the Inland Revenue Department as a stepping stone towards knowledge management through ontology. The work can be a foundation for the future works in this domain in good governance, data sharing, interoperability and transparency for the government of Nepal.

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Comparative Analysis of Methodologies for Domain Ontology Development: A Systematic Review

Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain. Managing knowledge in the IWs domain is complex, but promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established. In this sense, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management of the domain of discourse. There are many methodologies that have been proposed by several researchers during the last decade. However, designing a domain ontology for IWs needs a well-defined ontology development methodology. Therefore, in this article, a survey has been conducted to compare ontology development methodologies between 2015 and 2020. The purpose of this survey is to identify limitations and benefits of previously developed ontology development methodologies. The criteria for the comparison of methodologies has been derived from evolving trends in literature. Our findings give some guidelines that help to define a suitable methodology for designing any domain ontology under the domain of interlocking institutional worlds.

The UDSA ontology: An ontology to support real time urban sustainability assessment

Urban sustainability assessment frameworks have emerged during the past decade to address holistically the complexity of the urban landscape through a systems approach, factoring in environmental, social and economic requirements. However, the current assessment schemes are (a) static in nature, and as such don't reflect the dynamic and real-time nature of urban artefacts, (b) are not grounded in semantics (e.g. BIM and GIS), and (c) are at best used to assist in regulatory compliance, for instance in energy design, to meet increasingly stringent regulatory requirements. Information and communication technologies provide a new value proposition capitalizing on the Internet of Things (IoT) and semantics to provide real-time insights and inform decision making. Consequently, there is a real need in the field for data models that could facilitate data exchange and handle data heterogeneity. In this study, a semantic data model is considered to support near real-time urban sustainability assessment and enhance the semantics of sensor network data. Based on an extensive review of urban sustainability assessment frameworks and ontology development methodologies, the Urban District Sustainability Assessment (UDSA) ontology has been developed and validated using real data from the site of “The Works”, a newly refurbished neighbourhood in Ebbw Vale, Wales. This novel approach reconciles several domain-specific ontologies within one high-level ontology that can support the creation of real-time urban sustainability assessment software. In addition, this information model is aligned with 29 authoritative urban sustainability assessment frameworks, thus providing a useful resource not only in urban sustainability assessment, but also in the wider smart cities context.

More Effective Ontology Authoring with Test-Driven Development and the TDDonto2 Tool

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based specification of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated by users, which demonstrated that modellers make significantly fewer errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that TDD is a promising approach in an ontology development methodology.