Ontology-driven Approach Research Articles

Ontology-Driven Architecture for Managing Environmental, Social, and Governance Metrics

The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the demand for transparency and stakeholder engagement present significant challenges, given the lack of standardized ESG metrics in certain areas. Recently, the use of ontology-driven architectures has gained attention for their ability to encapsulate domain knowledge and facilitate integration with decision-support systems. This paper proposes a knowledge graph in the ESG metric domain to assist corporations in cataloguing and navigating ESG reporting requirements, standards, and associated data. Employing a design science methodology, we developed an ontology that serves as both a conceptual foundation and a semantic layer, fostering the creation of an interoperable ESG Metrics Knowledge Graph (ESGMKG) and its integration within operational layers. This ontology-driven approach promises seamless integration with diverse ESG data sources and reporting frameworks, while addressing the critical challenges of metric selection, alignment, and data verification, supporting the dynamic nature of ESG metrics. The utility and effectiveness of the proposed ontology were demonstrated through a case study centred on the International Financial Reporting Standards (IFRS) framework that is widely used within the banking industry.

DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts

The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach.

BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning

Context:Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated. Objective:This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns. Method:The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics. Conclusions:This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis. Result:Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.

Onto-CARMEN: Ontology-driven approach for Cyber–Physical System Security Requirements meta-modelling and reasoning

In the last years, Cyber–physical systems (CPS) have attracted substantial mainstream, especially in the industrial sector, since they have become the focus of cyber-attacks. CPS are complex systems that encompass a great variety of hardware and software components with a countless number of configurations and features. For this reason, the construction, validation, and diagnosis of security in CPS become a major challenge. An invalid security requirement for the CPS can produce partial or incomplete configuration, even misconfigurations, and hence catastrophic consequences. Therefore, it is crucial to ensure the validation of the security requirements specification from the earlier design stages. To this end, Onto-CARMEN is proposed, a semantic approach that enables the automatic verification and diagnosis of security requirements according to the ENISA and OWASP recommendations. Our approach provides a mechanism for the specification of security requirements on top of ontologies, and automatic diagnosis through semantic axioms and SPARQL rules. The approach has been validated using security requirements from a real case study.

An ontology-based framework for semantic geographic information systems development and understanding

Geographic information systems (GIS) are a widely used approach for geodata manipulation, analysis, and geoinformation visualization. Although the issue of semantic heterogeneity for GIS applications has been widely addressed, the development of flexible semantic GIS systems can still be improved, and GIS interfaces can be further unified to enhance system understanding among users of various GIS systems. This study proposes an ontology-based semantic GIS conceptual framework that can seamlessly combine ontology models, GIS elements and user actions for semantic GIS application development. In addition, a GIS components ontology that encompasses the five fundamental components of GIS is proposed to integrate GIS elements that can perceive GIS operations and processes semantically and to support customized GIS system designs. Moreover, a semantic WebGIS implementation framework is designed on the basis of the proposed conceptual framework to facilitate semantic WebGIS development. The implementation uses two common application scenarios regarding urban change analysis and user interaction. The former performs semantic retrieval for urban change analysis with system understanding, whereas the latter conducts online thematic mapping with semantic interpretation. The study contributes in terms of innovation in the design of a unified GIS components ontology and successful implementation of a semantic GIS system using the ontology-driven approach, thereby providing a convincing demonstration of the latter and showing the potential for semantic GIS application paradigms.

ONTOLOGY-DRIVEN MEANS FOR PROCESSING AND PRESENTATION OF LARGE ARRAYS OF UNSTRUCTURED TEXTS

The subject of the article's research is methods of ontology-driven processing and presentation of unstructured texts in a global environment. The goal of the work is to improve the efficiency of automatic search, classification and selection of the necessary information contained in electronic publications in an unstructured form by developing a model, method and means of automated processing and transdisciplinary presentation of text documents created in Ukrainian, Russian and English languages. In accordance with the goal, the following tasks were set: to conduct an overview of models of processing and presentation of unstructured texts, to identify the advantages of information systems with an ontology-driven architecture, to develop a conceptual model and means for automated processing and transdisciplinary presentation of text documents, improve the method of recursive reduction, with the help of developed method and means to process a large array of unstructured texts (in particular information base of knowledge of scientific products (IBK SP), educational programs, as well as educational and research projects of the National Center "Junior Academy of Sciences of Ukraine"), to present them in a form that allows to reveal the quality and completeness of the knowledge system available in the databases and, thus, experimentally to confirm the effectiveness of the developed means. The conducted research is based on the following methods: systemological classification, algebraic-logical and axiomatic methods, the method of recursive reduction. The following results were obtained: the existing models of processing and presentation of unstructured texts were described, the technological aspects of the ontology-driven approach to the processing and presentation of large arrays of network documents were improved, the technology of forming ontologies of documents based on the representation of their taxonomies, connections between their contexts and multiple representations was developed, which provides effective search for information in unstructured texts. Conclusions: The advantages of ontology-driven means for processing and presentation unstructured texts are substantiated. The method of recursive reduction by constructing a transformation, which leads to the predicative form of ontologically given descriptions of information, gained further development. The idea of effective search, processing, classification and selection of the necessary information has gained further development.

Sprachdatenbasierte Modellierung von Wissensnetzen in der mittelalterlichen Romania (ALMA): Projektskizze

Abstract We present ALMA, a new research project aimed at investigating the interaction between language and knowledge practices from AD 1100 to 1500. Our primary question is how Medieval Italian, French, Occitan, Catalan, and Spanish developed into languages of knowledge and scholarship (German Wissen(schafts)sprachen) in permanent opposition to and exchange with the predominant Latin (but also with Arab, Greek, and Hebrew). Focusing on two domains, medicine and law, the project combines linguistics, text philology, and the history of science with the Digital Humanities and ontology engineering. ALMA will create two multi-lingual, domain-specific text corpora by integrating text editions of hitherto unedited manuscripts and incunabula, and digitized printed editions. Our corpus-linguistic exploration of the ALMA corpora will provide the basis for lexical-semantic studies that analyze emerging knowledge networks and the depth of their linguistic representations. We hypothesize that language evolution and the development of more complex linguistic structures will allow for measuring the impact of knowledge practices on medieval vernacular languages. We will trace the dissemination of lexical material across languages, language varieties, cultural spaces, and periods. This will enable us to follow specific vernacular communication channels. We will use cutting-edge technologies to compile, publish, and share our findings, and to model them in the form of historicized ontologies and Linked Data. Our onomasiological, ontology-driven approach will result in the creation of domain models that can be re-used within the Semantic Web. This has great potential to be relevant for researchers from different disciplines.

Outstanding issues in ensuring more stable international deliveries of Russian fuel and energy resources to the segments of the world economy available for exports

Relevance. The paper discusses the digital ontology-driven approach to the development of a quasi-integrated supersystem. The supersystem includes the energy sector and its supporting elements of the financial and economic system of Russia and a group of friendly countries who are consumers of Russian fuel and energy resources (FER). The approach is employed to develop measures that ensure national and international economic and energy security. International deliveries of Russian fuel and energy resources to the segments of the world economy available for exports can become unstable due to the current crisis in international economic relations. The use of the digital ontology-driven approach is a response to the need to analyze the underlying processes and predict whether such instability can become critical in the future. This is done in conjunction with configuring properly the operation of the Russian infrastructure of production, transport, storage, processing, and deliveries of fuel and energy resources. To that end, they are to be adjusted so as to align with the transformation that the destinations and structure of Russian exports are undergoing. Problem statement. We argue for the adoption of the digital ontology-driven approach to the development of a quasi-integrated supersystem. The supersystem includes the energy sector and its supporting elements of the financial and economic system of Russia and a group of friendly countries who are consumers of Russian fuel and energy resources being parties to economic unions and bilateral agreements between the countries. We identify different variants of the impact of effected discriminatory practices and the state of commodity and financial markets. We study this impact as applied to 1) Russian energy companies supplying FERs abroad, 2) consumers of Russian FERs in Russia and abroad, 3) companies transporting FERs abroad and using other countries for transit through them, and 4) manufacturers of equipment for the energy sector. Purpose of the study. We aim to identify conceptual approaches clarifying the effect of macroeconomic factors on 1) Russian energy companies

A hybrid deep learning and ontology-driven approach to perform business process capability assessment

Enterprises are constantly transforming to adapt to an ever-changing and competitive environment. In this context, assessments allow to understand the state of different organisational aspects before performing transformation activities. One of these aspects is the capability of business processes. Evaluating the quality of business processes is relevant to guide improvement initiatives, considering that the way that processes are designed and executed in organisations has direct impact on the quality of products and services. However, assessments are expensive in terms of resources if they are performed by humans. In this sense, recent trends in Artificial Intelligence provide means to improve process capability assessment through the automation of some of its tasks. Following this line, this work presents a method to perform process capability assessment using raw text as input data with the aid of a smart system, able to reduce the need of human intervention to provide reliable assessment results. For this purpose, we introduce a hybrid approach to perform assessments in enterprises using text data as assessment evidence. The method combines the Long Short-Term Memory Network (LSTM) approach and the use of an Ontology named Process Capability Assessment Ontology (PCAO), which also contains a set of rules to calculate process attribute ratings, capability levels, among other aspects. The approach is grounded on the Smart Assessment Framework, a conceptual model devised to guide the development of intelligent assessments in enterprises. We introduce a demonstration of the assessment of a process based on the management of chemical samples from a research institute.

Ontology-Driven Approach for Liver MRI Classification and HCC Detection

Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.

СИСТЕМОТЕХНІЧНА РЕАЛІЗАЦІЯ ТЕОРЕТИКО-МЕТОДОЛОГІЧНИХ ЗАСАД ТРАНСДИСЦИПЛІНАРНОЇ ІНТЕЛЕКТУАЛЬНОЇ ІНФОРМАЦІЙНО-АНАЛІТИЧНОЇ СИСТЕМИ ДЛЯ РЕАБІЛІТАЦІЇ ПРИ ПАНДЕМІЇ (TISP)

I ntelligent Information Systems (IIS) is the next step in the evolution of information processing systems. The intelligent system must provide the opportunity to use modern technologies and tools for data integration, their processing and extraction of knowledge in explicit or implicit form. The use of ontologies is provided an eff ective analysis of information resources and their use in IIS, especially in areas of human activity that are evolving and changing rapidly. The implementation of an eff ective rehabilitation system based on evidence-based medicine and uniform standards and rules is an important component of Ukraine’s health care system in the time of a pandemic. Subject orientation of the transdisciplinary intellectual information-analytical system and creation of separate functional confi gurations of the system is aimed at use in the fi eld of medical rehabilitation. Objective of the research was the subject orientation of the transdisciplinary intellectual information-analytical system and the creation of individual functional confi gurations of the system for use in the fi eld of medical rehabilitation. The theoretical and methodological basis of “Transdisciplinary intelligent information and analytical system for the rehabilitation processes support in a pandemic (TISP)” is the conceptual and methodological principles of knowledge-oriented processing of natural language texts. They consist of models and methods that include methods of construction and processing of ontologies of the subject area, which are based on methods of construction and analysis of the structure of growing pyramidal networks; logical-mathematical models of natural language texts processing on the basis of ontologies and growing pyramidal networks; local-statistical method of concept allocation in growing pyramidal networks, which allows forming the concepts of minimum length with maximum support for polynomial time; methods of semantic processing of natural language texts, which use the mechanisms of adjacency of facts, to form a system of «questions and answers» on the content of texts. In the process of performing the grant work, a strategy for expanding the functionality and subject orientation of TISP was developed. TISP platform was implemented with a component architecture of cognitive services, which provides transdisciplinary consolidation of all specialists who need to be involved in the rehabilitation of a particular patient. The International Classifi cation of Functioning, Disability and Health, (ICF) and the White Book on Physical and Rehabilitation Medicine in Europe, which defi nes the nature, scope and parameters of physical and rehabilitation medicine in Europe, have been developed in the form of interactive documents. The ontology-driven approach to the operability of the interaction between the doctor-rehabilitation specialist and the patient promptly implements the entire technological chain from determining the patient’s condition to the choice and appointment by the doctor of a specifi c set of rehabilitation procedures. The use of an ontology-driven approach provides fl exibility in the subject orientation of TISP and the building of individual functional confi gurations of the system.

Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

BackgroundDyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data.MethodsThe Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope.ResultsOur effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data.ConclusionUsing logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.

Ontology-driven approach for KPI meta-modelling, selection and reasoning

A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain assumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally conceptualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators.

Domain-specific requirements analysis framework: ontology-driven approach

Requirement engineering (RE) is the first track in the course of software development that represents and analyses the problem domain for suitable solutions. This improves the quality of the software development and decreases the risk of budget overrun, delays and project failure. However, traditional methods are inadequate to address different problems in RE. Prime challenges among those are lack of interoperability between heterogeneous requirements models, less focus on validation of requirements, and inability to reuse similar requirement specification in different domains. To solve these challenges, semantic approaches are needed to represent commonalities and variability in requirements across different applications and domains. This paper is aimed to address this issue. A requirement analysis framework named as Ontology-driven requirement analysis (ODRA) is proposed in this paper. Proposed ontology-driven specification provides common semantics towards different requirements. Proposed framework also comprises Formal Scenario descriptions and cause-effect-dependency graphs to conduct an early and a detailed analysis of requirements. It is capable to represent 5W1H characteristics (What, Why, Who, When, Where and How) of requirements. Further, it devises a set of key validation rules and a customized rule-based reasoner to validate consistency, unambiguity, and traceability in requirement specifications.

An ontology-driven approach for modelling TMS fuel consumption information subsystem

The use of information technologies for the automotive transport and logistics management has opened game-changing opportunities for business, providing solutions to critical problems of sustainable development in turbulent environments. Today, the data for transport operations can be processed and used for the managerial decision-making, actually, on-line. However, despite abundance of TMSs on the market, many transport companies use them for solving operating management problems only. There are also lots of companies which do not use TMSs at all. Practice shows that managers of numerous transport companies have a little idea of how the records management data can be transformed into the awareness of improving efficiency of the transportation process. In our opinion it may be explained by the fact that different management levels use the knowledge from various domains, such as transport, IT, economic, and financial areas. Accordingly, the transformation of contemporaneous data into the managerial decisions requires an approach which is discussed in this paper using an example of introduction of TMS fuel consumption information subsystem.

Development of an approach to using a style in software engineering

An ontology-driven approach to applying styles in software engineering is developed in the study. The essence of the approach is to use ontology not only to represent styles but also to control the use of styles when creating and maintaining software. For such purposes, appropriate ontology and developer support tools are provided to represent a style, and tools (reasoners) are created on the ground of the ontological knowledge base to control the application of the style in work products of the software lifecycle phases. Based on descriptive logic, the knowledge base contains two components – terminological (TBox) and factual (ABox). The first component was created in advance by performing a domain analysis. The second component was created when analysing the representation of the corresponding work product. For the purpose of typification in the context of the developed approach, templates for the kernel ontology of the software engineering style were created by analysing the concept of style in different domains. The basic characteristics of style as a domain-independent concept, which are presented in the templates, are formulated in the article. In this case, the Work Product Pattern Application pattern of the Unified Foundational Ontology is used to select the number of templates needed to represent the style. The pattern describes actions that may exist regarding the Work Product style. An example of using the approach is considered by investigating the proposed ontology-driven method for programming style application in software engineering and building the architecture of the tool that implements it. Protege use shows how to build the ontology of a programming style and programmer support. The architecture of the tool for controlling the application of style is developed and implemented in a work product of the design phase – a programme text. The basis of the architecture is the knowledge base of the corresponding style. The terminological component of the knowledge base contains information regarding programming languages and styles, and it is created in advance by the ontology developer. The actual component is created by the reasoner for each representation of the work product – the programme text. The tools developed in the context of the proposed approach automate the processes that take place when applying styles to work products of the software lifecycle phases.

SHOAL

E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platforms organizes items into an ontology structure. However, the ontology-driven approach is subject to costly manual maintenance and often does not capture user's search intention, particularly when user searches by her personalized needs rather than a universal definition of the items. Observing that search queries can effectively express user's intention, we present a novel large-Scale Hierarchical taxOnomy via grAph based query coaLition ( SHOAL ) to bridge the gap between item taxonomy and user search intention. SHOAL organizes hundreds of millions of items into a hierarchical topic structure . Each topic that consists of a cluster of items denotes a conceptual shopping scenario, and is tagged with easy-to-interpret descriptions extracted from search queries. Furthermore, SHOAL establishes correlation between categories of ontology-driven taxonomy, and offers opportunities for explainable recommendation. The feedback from domain experts shows that SHOAL achieves a precision of 98% in terms of placing items into the right topics, and the result of an online A/B test demonstrates that SHOAL boosts the Click Through Rate (CTR) by 5%. SHOAL has been deployed in Alibaba and supports millions of searches for online shopping per day.

Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model.

BackgroundThe All Of Us Research Program (AOU) is building a nationwide cohort of one million patients’ EHR and genomic data. Data interoperability is paramount to the program’s success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model.Methods and resultsHere, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites’ existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP’s visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects.

Constraint specifications for domain-specific systems: ontology-driven approach

Well-formed Domain Specific Modelling Languages (DSML) are devised based on well-defined sets of syntaxes and semantics. These precisions are obtained raising at abstraction levels in domain specifications. Yet, appropriate representations of constraints are also important to limit meanings of generic concepts to different abstraction levels in a DSML. To address this issue, in this paper, a constraint specification language is developed. This proposed language is capable to restrict meanings of general concepts represented in an upper level ontology - Generalised Ontology Modelling (GOM) (Banerjee and Sarkar, 2016b) to domain specific systems in a systematic way. Further, several automated methods impose distinct constraints at different levels of abstractions in domain specific modelling. These methods also validate distinct constructs and constraints in domain specific systems against general concepts of GOM. The applicability of the proposed work is proved using couple of case studies based on applications in data-intensive and service-based domains.

Constraint specifications for domain-specific systems: ontology-driven approach

Well-formed Domain Specific Modelling Languages (DSML) are devised based on well-defined sets of syntaxes and semantics. These precisions are obtained raising at abstraction levels in domain specifications. Yet, appropriate representations of constraints are also important to limit meanings of generic concepts to different abstraction levels in a DSML. To address this issue, in this paper, a constraint specification language is developed. This proposed language is capable to restrict meanings of general concepts represented in an upper level ontology - Generalised Ontology Modelling (GOM) (Banerjee and Sarkar, 2016b) to domain specific systems in a systematic way. Further, several automated methods impose distinct constraints at different levels of abstractions in domain specific modelling. These methods also validate distinct constructs and constraints in domain specific systems against general concepts of GOM. The applicability of the proposed work is proved using couple of case studies based on applications in data-intensive and service-based domains.