Upper-level Ontology Research Articles

Digital Representation of Materials Testing Data for Semantic Web Analytics: Tensile Stress Relaxation Testing Use Case

This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web.

Behavioral change interventions, theories, and techniques to reduce physical inactivity and sedentary behavior in the general population: a scoping review

BackgroundWorldwide, physical inactivity (PIA) and sedentary behavior (SB) are recognized as significant challenges hindering the achievement of the United Nations (UN) sustainable development goals (SDGs). PIA and SB are responsible for 1.6 million deaths attributed to non-communicable diseases (NCDs). The World Health Organization (WHO) has urged governments to implement interventions informed by behavioral theories aimed at reducing PIA and SB. However, limited attention has been given to the range of theories, techniques, and contextual conditions underlying the design of behavioral theories. To this end, we set out to map these interventions, their levels of action, their mode of delivery, and how extensively they apply behavioral theories, constructs, and techniques.MethodsFollowing the scoping review methodology of Arksey and O’Malley (2005), we included peer-reviewed articles on behavioral theories interventions centered on PIA and SB, published between 2010 and 2023 in Arabic, French, and English in four databases (Scopus, Web of Science [WoS], PubMed, and Google Scholar). We adopted a framework thematic analysis based on the upper-level ontology of behavior theories interventions, Behavioral theories taxonomies, and the first version (V1) taxonomy of behavior change techniques(BCTs).ResultsWe included 29 studies out of 1,173 that were initially screened/searched. The majority of interventions were individually focused (n = 15). Few studies have addressed interpersonal levels (n = 6) or organizational levels (n = 6). Only two interventions can be described as systemic (i.e., addressing the individual, interpersonal, organizational, and institutional factors)(n = 2). Most behavior change interventions use four theories: The Social cognitive theory (SCT), the socioecological model (SEM), SDT, and the transtheoretical model (TTM). Most behavior change interventions (BCIS) involve goal setting, social support, and action planning with various degrees of theoretical use (intensive [n = 15], moderate [n = 11], or low [n = 3]).Discussion and conclusionOur review suggests the need to develop systemic and complementary interventions that entail the micro-, meso- and macro-level barriers to behavioral changes. Theory informed BCI need to integrate synergistic BCTs into models that use micro-, meso- and macro-level theories to determine behavioral change. Future interventions need to appropriately use a mix of behavioral theories and BCTs to address the systemic nature of behavioral change as well as the heterogeneity of contexts and targeted populations.

Zero Defect Manufacturing ontology: A preliminary version based on standardized terms

The global transition from traditional manufacturing systems to Industry 4.0 compatible systems has already begun. Therefore, the digitization of the manufacturing systems across the globe is increasing with exponential growth which implies a significant increase in the volume and variety of the generated data. Industry 4.0 technologies are mostly data driven and therefore, manufacturers need to be equipped with the appropriate tools and skill sets to extract useful knowledge and insights from the plethora of data continually collected form shop floors. Furthermore, quality assurance is a key domain in manufacturing that uses almost all the industry 4.0 technologies and has great impact on the sustainability of a manufacturing systems. The latest approach to higher quality and manufacturing sustainability is named Zero Defect Manufacturing (ZDM). ZDM interest has spiked the last three years illustrating the need for an alternative quality assurance approach from the traditional such as Six Sigma and Lean manufacturing. Therefore, the goal of this paper is to create a ZDM ontology that can semantically align multiple software systems that interact in a ZDM ecosystem. The development of the proposed ZDM ontology was performed using the principles introduced by Industrial Ontology Foundry (IOF) and with the use of Basic formal ontology (BFO) as an upper level ontology. The proposed ontology was utilized in the Prediction Optimization Designer tool developed, to assist developers to create new projects reusing existing resources, or to respond to a specific challenge. The use case validation results show that the combination of Natural Language Processing (NLP) using Sentence-BERT and ontology-based search methods rooted in the ZDM ontology is a promising strategy to implement effective search engines for applications in the ZDM domain.

Ontology-based surgical workflow recognition and prediction

BackgroundSurgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and assist the surgical process, the system needs to monitor the intraoperative activities, understand the current situation in the operating room at any time, and anticipate the following possible situations. MethodsA structured representation of surgical process knowledge is a prerequisite for any applications in the intelligent operating room. For this purpose, a surgical process ontology, which is formally based on standard medical terminology (SNOMED CT) and an upper-level ontology (GFO), was developed and instantiated for a neurosurgical use case. A new ontology-based surgical workflow recognition and a novel prediction method are presented utilizing ontological reasoning, abstraction, and explication. This way, a surgical situation representation with combined phase, high-level task, and low-level task recognition and prediction was realized based on the currently used instrument as the only input information. ResultsThe ontology-based approach performed efficiently, and decent accuracy was achieved for situation recognition and prediction. Especially during situation recognition, the missing sensor information were reasoned based on the situation representation provided by the process ontology, which resulted in improved recognition results compared to the state-of-the-art. ConclusionsIn this work, a reference ontology was developed, which provides workflow support and a knowledge base for further applications in the intelligent operating room, for instance, context-aware medical device orchestration, (semi-) automatic documentation, and surgical simulation, education, and training.

Toward an ontology of tobacco, nicotine and vaping products.

Ontologies are ways of representing information that improve clarity and the ability to connect different data sources. This paper proposes an initial version of an ontology of tobacco, nicotine and vaping products with the aim of reducing ambiguity and confusion in the field. Terms related to tobacco, nicotine and vaping products were identified in the research literature and their usage characterised. Basic Formal Ontology was used as a unifying upper-level ontology to describe the domain, and classes with definitions and labels were developed linking them to this ontology. Labels, definitions and properties were reviewed and revised in an iterative manner until a coherent set of classes was agreed by the authors. Overlapping, but distinct classes were developed: 'tobacco-containing product', 'nicotine-containing product' and 'vaping device'. Subclasses of tobacco-containing products are 'combustible tobacco-containing product', 'heated tobacco product' and 'smokeless tobacco-containing product'. Subclasses of combustible tobacco-containing product include 'cigar', 'cigarillo', 'bidi' and 'cigarette' with further subclasses including 'manufactured cigarette'. Manufactured cigarettes have properties that include 'machine-smoked nicotine yield' and 'machine-smoked tar yield'. Subclasses of smokeless tobacco product include 'nasal snuff', 'chewing tobacco product', and 'oral snuff' with its subclass 'snus'. Subclasses of nicotine-containing product include 'nicotine lozenge' and 'nicotine transdermal patch'. Subclasses of vaping device included 'electronic vaping device' with a further subclass, 'e-cigarette'. E-cigarettes have evolved with a complex range of properties including atomiser resistance, battery power, properties of consumables including e-liquid nicotine concentration and flavourings, and the ontology characterises classes of product accordingly. Use of an ontology of tobacco, nicotine and vaping products should help reduce ambiguity and confusion in tobacco control research and practice.

The Sortal Concept in the Context of Biomedical Record Linkage.

Biomedical Record Linkage is especially designed for linking data of patients in different data repositories. An important question in this context is whether singling-out is sufficient for identifying a patient, and if not, what is in general required for identification. To provide hints for an answer, we will extend previous works on the concept of identity and extend the sortal concept, stemming from analytical philosophy and upper-level ontologies. A sortal is a concept that is associated with an identity criterion. For example, the concept "set" has the identity criterion "having the same members". Based on a description of a record linkage setting, we operationalize the sortal concept by providing a distinction between the digital representation of a person (d-sortal) and the person in flesh (b-sortal).

ICEO, a biological ontology for representing and analyzing bacterial integrative and conjugative elements

Bacterial integrative and conjugative elements (ICEs) are highly modular mobile genetic elements critical to the horizontal transfer of antibiotic resistance and virulence factor genes. To better understand and analyze the ongoing increase of ICEs, we developed an Integrative and Conjugative Element Ontology (ICEO) to represent the gene components, functional modules, and other information of experimentally verified ICEs. ICEO is aligned with the upper-level Basic Formal Ontology and reuses existing reliable ontologies. There are 31,081 terms, including 26,814 classes from 14 ontologies and 4128 ICEO-specific classes, representing the information of 271 known experimentally verified ICEs from 235 bacterial strains in ICEO currently and 311 predicted ICEs of 272 completely sequenced Klebsiella pneumoniae strains. Three ICEO use cases were illustrated to investigate complex joins of ICEs and their harboring antibiotic resistance or virulence factor genes by using SPARQL or DL query. ICEO has been approved as an Open Biomedical Ontology library ontology. It may be dedicated to facilitating systematical ICE knowledge representation, integration, and computer-assisted queries.

Developing an Ontology for Documenting Adverse Events While Avoiding Pitfalls.

Ontologies promise more benefits than terminologies in terms of data annotation and computer-assisted reasoning, by defining a hierarchy of terms and their relations within a domain. Here, we present central insights related to the development of an ontology for documenting events during interoperative neuromonitoring (IOM), for which we used the Basic Formal Ontology (BFO) as an upper-level ontology. This work has the following two goals: to describe the development of the IOM ontology and to guide the practice with respect to documenting of biomedical events, as available ontologies pose difficulties on certain issues. We address the following issues: (i) differentiate between the sets documentation, identification, continuant and explanation, understanding, occurrent as we had problems in applying the available ontology of adverse events, (ii) covering diseases and injuries in a consistent way, and (iii) deciding on which level to define relations.

A Novel Method for Ranking Knowledge Organization Systems (KOSs) Based on Cognition States .

The purpose of this article is to delineate the process of evolution of know­ledge organization systems (KOSs) through identification of principles of unity such as internal and external unity in organizing the structure of KOSs to achieve content storage and retrieval purposes and to explain a novel method used in ranking of KOSs by proposing the principle of rank unity. Different types of KOSs which are addressed in this article include dictionaries, Roget’s thesaurus, thesauri, micro, macro, and meta-thesaurus, ontologies, and lower, middle, and upper-level ontologies. This article relied on dialectic models to clarify the ideas in Kant’s know­ledge theory. This is done by identifying logical relationships between categories (i.e., Thesis, antithesis, and synthesis) in the creation of data, information, and know­ledge in the human mind. The Analysis has adapted a historical methodology, more specifically a documentary method, as its reasoning process to propose a conceptual model for ranking KOSs. The study endeavors to explain the main elements of data, information, and know­ledge along with engineering mechanisms such as data, information, and know­ledge engineering in developing the structure of KOSs and also aims to clarify their influence on content storage and retrieval performance. KOSs have followed related principles of order to achieve an internal order, which could be examined by analyzing the principle of internal unity in know­ledge organizations. The principle of external unity leads us to the necessity of compatibility and interoperability between different types of KOSs to achieve semantic harmonization in increasing the performance of content storage and retrieval. Upon introduction of the principle of rank unity, a ranking method of KOSs utilizing cognition states as criteria could be considered to determine the position of each know­ledge organization with respect to others. The related criteria of the principle of rank unity- cognition states- are derived from Immanuel Kant’s epistemology. The research results showed that KOSs, while having defined positions in cognition states, specific principles of order, related operational mechanisms, and related principles of unity in achieving their specific purposes, have benefited from the developmental experiences of previous KOSs, and further, their developmental processes owe to the experiences and methods of their previous generations.

A Genealogy Ontology Containing Turkish Kinship Terms for Traditional Families

Today, with the innovations brought by technology, many information, official documents, correspondence, and evaluation data kept in archives have been transferred to digital environment. One of the areas where the digitalization process has started and continues rapidly is population and registry information. However, the existing systems are sometimes insufficient to present the log information clearly. Existing systems used in relevant government institutions and libraries can only present kinship ties in a hierarchical structure due to the lack of necessary infrastructure resources. The family tree created is insufficient to establish parallel relationships. However, it is obvious that in most judicial and institutional cases, it will shed light on the current situation in parallel kinship relations. In addition, existing genealogies are insufficient to reveal semantic patterns between the concepts of family, person, place, time, and event while listing kinship relations. In addition to all these, the existence of cultural differences between nations and the expression of kinship relations with different terms require the definition of a common family tree. In this study, semantic web technologies, ontologies, and related data, which have been widely used in many fields in recent years, are structured to provide a clearer conceptualization of kinship relations. The study also discusses the alignment and reusability of the developed genealogical ontology with upper-level ontologies so that it can be used more broadly. Keywords: Genealogical Ontology, Semantic Web Technologies, Linked Data, Protégé, Ontology Methodologies DOI: 10.7176/JSTR/7-10-10

Towards a Semantic Data Harmonization Federated Infrastructure.

Data integration is an increasing need in medical informatics projects like the EU Precise4Q project, in which multidisciplinary semantically and syntactically heterogeneous data across several institutions needs to be integrated. Besides, data sharing agreements often allow a virtual data integration only, because data cannot leave the source repository. We propose a data harmonization infrastructure in which data is virtually integrated by sharing a semantically rich common data representation that allows their homogeneous querying. This common data model integrates content from well-known biomedical ontologies like SNOMED CT by using the BTL2 upper level ontology, and is imported into a graph database. We successfully integrated three datasets and made some test queries showing the feasibility of the approach.

Sup_Ont

A domain-independent conceptual model that aims to be highly reusable across specific domain applications is provided by upper-level ontologies which usually describe abstract concepts. In this paper, the authors proposed Sup_Ont, a fundamental upper ontology. In this ontology, the structure of the universe shows the concept of reality that is defined to have an existence which is known as truth. The devised super ontology and hence the domain ontologies can be reused across applications because of the generalized representation scheme used that is an EHCPR. An extended hierarchical censored production rules (EHCPRs) system is a knowledge representation system for reasoning with real-life problems and a step towards a generalized representation system. An EHCPR is a unit of knowledge resulting in a knowledge base that shows modularity and hierarchy. Extended hierarchical censored production rules (EHCPRs) have been used to represent the knowledge in intelligent systems.

A Dialectic Perspective on the Evolution of Thesauri and Ontologies

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget’s Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface.

BackgroundEfforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.ObjectiveWe aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.MethodsWe developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.ResultsWe developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.ConclusionsTranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

A Semantic Matching Strategy for Very Large Knowledge Bases Integration

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.

OntoGamif: A modular ontology for integrated gamification

Gamification is known as the use of game design elements in non-game contexts. It has gained a great interest from both industrials and academics during the last years due to its role in increasing engagement of people in several sectors including business, learning, crowdsourcing and enterprise in formation systems. However, due to its inherent multi-disciplinarity, gamification lacks an integrated view and a holistic and consensual agreement on its foundational concepts among the stakeholders. In this context we propose OntoGamif (Ontology of Gamification), a modular ontology for the gamification domain of interest covering topics concerning, for instance, target users, organizational structures, ethical issues, and psychological factors. These are organized as seven linked modular sub-ontologies that can be used also independently to support the work of gamification designers implementing personalized gamified solutions. The process used to build OntoGamif was inspired by some of the most used ontology engineering methodologies. Moreover, we discuss the multi-perspective approach in evaluating OntoGamif including experts’ validation, case study analysis, comparison to a corpus and a mapping of OntoGamif to the SUMO upper level ontology.

A time-indexed mereology for SUMO

While the period of time during which a subprocess occurs is precisely the time during which the part-whole relation with its main process takes place, part-whole relations between objects do not obey such a rule. The parts of an object can exist before the object is conformed as such, and can survive its dismantlement. In fact, there are no means for knowing when, during the existence of the part and the whole, their parthood relation holds unless an explicit account of time is represented. A time-indexed mereology characterizes how objects gain and lose parts over time by associating a time index to their part-whole relations. Keeping an account of when objects lose or gain parts is necessary for the correct representation of their spatial location and their participation in processes. Upper-level ontologies characterize the properties of the most basic, domain-independent entities, such as time, space, objects and processes. Two upper-level ontologies broadly used are The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) and The Suggested Upper Merged Ontology (SUMO). However, while DOLCE provides a first-order time-indexed mereology for structuring its entities over time, SUMO provides a weaker axiomatization that does not represent the rules that determine how the mereological structure of objects evolve through time in the real world. This work proposes a first-order logic time-indexed mereology for SUMO based on its current representation of objects, time, and temporal location, thereby characterizing how objects gain and lose parts over time. The proposed theory sets the stage for the development of a time-indexed theory of spatial location, and for the representation of temporal restrictions on the participation of objects in processes. The time-indexed mereology of DOLCE and the proposed theory are formally compared, and their relative strength established by using ontology mapping. In order to achieve such a comparison, the representations of time, and temporal location of both upper-level ontologies are also formally compared.

Integrating ABCD and DarwinCore: Toward a better foundation for biodiversity information standards

For the last 15 years, Biodiversity Information Standards (TDWG) has recognized two competing standards for organism occurrence data, ABCD (Access to Biological Collections Data; Holetschek et al. 2012) and DarwinCore (Wieczorek et al. 2012). These two representations emerged from contrasting strategies for mobilizing information about organism occurrences (also commonly called species occurrence data). ABCD was capable of representing details of more kinds of information, but was necessarily more complicated. DarwinCore, on the other hand, was simpler but more limited in its ability to represent data of different kinds and formats. TDWG endorsed both standards because the different projects and communities that generated them remained dedicated to their different strategies and tool sets, and the Global Biodiversity Information Facility (GBIF) developed the ability to integrate data published in either standard. Since their inceptions, DarwinCore and ABCD have become more similar. DarwinCore has gotten more complicated through the addition of terms and has begun to assign terms to classes. ABCD is now expressed in RDF (Resource Description Framework), potentially enabling re-use of terms with alternative structures among classes. At the same time, methodologies for conceptual modeling and representing complex scientific data have continued to evolve. In particular, a suite of modeling and data representation methods related to linked data and the semantic web, i.e., RDF, SKOS (Simple Knowledge Organization System), and OWL (web Ontology Language), promise to make it easier for us to reconcile shared concepts among different representations or schemas. A mapping between ABCD 2.1 and DarwinCore has existed since before 2005.*1 ABCD 3.0 and DarwinCore are both now represented in RDF. In addition, the BioCollections Ontology (BCO) covers many of the shared concepts and is derived from the Basic Formal Ontology (BFO), an upper level ontology that has oriented many other biomedical ontologies. Reconciling ABCD and DarwinCore through alignment with BCO (in the OBO Foundry; Smith et al. 2007) would better connect TDWG standards to other domains in biology. We appreciate that many working scientists and data managers perceive ontologies as overly complicated. To mitigate the steep learning curve associated with ontologies, we expect to create simpler application profiles or schemas to guide and serve narrower communities of practice within the wider biodiversity domain. We also plan to integrate the current work of the Taxonomic Names and Concepts Interest Group and thereby eliminate the redundancy between DarwinCore and Taxonomic Concepts Transfer Schema (TCS; Kennedy et al. 2006). At the time of this writing, we have only agreements from the authors (i.e., conveners of relevant TDWG Interest Groups and other key stakeholders) to collaborate in pursuit of these common goals. In this presentation we will give a more detailed description of our objectives and products, the methods we are using to achieve them, and our progress to date.

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.