Conceptual Modeling Languages Research Articles

Universal conceptual modeling: principles, benefits, and an agenda for conceptual modeling research

The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling.

Evaluating quality of ontology-driven conceptual models abstractions

The complexity of an (ontology-driven) conceptual model highly correlates with the complexity of the domain and software for which it is designed. With that in mind, an algorithm for producing ontology-driven conceptual model abstractions was previously proposed. In this paper, we empirically evaluate the quality of the abstractions produced by it. First, we have implemented and tested the last version of the algorithm over a FAIR catalog of models represented in the ontology-driven conceptual modeling language OntoUML. Second, we performed three user studies to evaluate the usefulness of the resulting abstractions as perceived by modelers. This paper reports on the findings of these experiments and reflects on how they can be exploited to improve the existing algorithm.

Assessing the value of ontologically unpacking a conceptual model for human genomics

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.

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.

Preserving conceptual model semantics in the forward engineering of relational schemas

Forward engineering relational schemas based on conceptual models (in languages such as UML and ER) is an established practice, with several automated transformation approaches discussed in the literature and implemented in production tools. These transformations must bridge the gap between the primitives offered by conceptual modeling languages on the one hand and the relational model on the other. As a result, it is often the case that some of the semantics of the source conceptual model is lost in the transformation process. In this paper, we address this problem by forward engineering additional constraints along with the transformed schema (ultimately implemented as triggers). We formulate our approach in terms of the operations of “flattening” and “lifting” of classes to make our approach largely independent of the particular transformation strategy (one table per hierarchy, one table per class, one table per concrete class, one table per leaf class, etc.). An automated transformation tool is provided that traces the cumulative consequences of the operations as they are applied throughout the transformation process. We report on tests of this tool using models published in an open model repository.

Design and evaluation of a collaborative UML modeling environment in virtual reality.

Modeling is a key activity in conceptual design and system design. Through collaborative modeling, end-users, stakeholders, experts, and entrepreneurs are able to create a shared understanding of a system representation. While the Unified Modeling Language (UML) is one of the major conceptual modeling languages in object-oriented software engineering, more and more concerns arise from the modeling quality of UML and its tool-support. Among them, the limitation of the two-dimensional presentation of its notations and lack of natural collaborative modeling tools are reported to be significant. In this paper, we explore the potential of using virtual reality (VR) technology for collaborative UML software design by comparing it with classical collaborative software design using conventional devices (desktop PC/laptop). For this purpose, we have developed a VR modeling environment that offers a natural collaborative modeling experience for UML Class Diagrams. Based on a user study with 24 participants, we have compared collaborative VR modeling with conventional modeling with regard to efficiency, effectiveness, and user satisfaction. Results show that the use of VR has some disadvantages concerning efficiency and effectiveness, but the user's fun, the feeling of being in the same room with a remote collaborator, and the naturalness of collaboration were increased.

Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing

In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel.

MetaMorph: formalization of domain-specific conceptual modeling methods—an evaluative case study, juxtaposition and empirical assessment

Models have evolved from mere pictures supporting human understanding and communication to sophisticated knowledge structures processable by machines and establish value through their processing capabilities. This entails an inevitable need for computer-understandable modeling languages and causes formalization to be a crucial part in the lifecycle of engineering a modeling method. An appropriate formalism must be a means for providing a structural definition to enable a theoretical investigation of conceptual modeling languages and a unique, unambiguous way of specifying the syntax and semantics of an arbitrary modeling language. For this purpose, it must be generic and open to capturing any domain and any functionality. This paper provides a pervasive description of the formalism MetaMorph based on logic and model theory—an approach fulfilling the requirements above for modeling method engineering. The evaluation of the formalism is presented following three streams of work: First, two evaluative case studies illustrate the applicability of MetaMorph formalism concept by concept on the modeling language ProVis from the domain of stochastic education and the well-known Entity-Relationship language. ProVis as well as ER comprise only a few objects and relation types but with high interconnection and expressive power and are therefore considered interesting specimens for formalization. Second, a comprehensive juxtaposition of MetaMorph to three other formalization approaches based on different foundational theories is outlined concept by concept to underpin the formalism design. Third, an empirical evaluation has been performed, assessing the usability and adequacy of the formalism within a classroom assessment. The results allow for conclusions on the completeness, intuitiveness, and complexity as well as on interdependencies with engineers’ skills.

Ontologically correct taxonomies by construction

Taxonomies play a central role in conceptual domain modeling, having a direct impact in areas such as knowledge representation, ontology engineering, and software engineering, as well as knowledge organization in information sciences. Despite this, there is little guidance on how to build high-quality taxonomies, with notable exceptions being the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules on the formation of taxonomic structures. In this paper, we show how to leverage the formal rules underlying these techniques in order to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. In addition to considering the ontological micro-theory of endurant types underlying OntoClean and OntoUML, we also employ the MLT (Multi-Level Theory) micro-theory of high-order types, which allows us to address multi-level taxonomies based on the powertype pattern. To validate our proposal, we formalize the model building operations as a graph grammar that incorporates both micro-theories. We apply automatic verification techniques over the grammar language to show that the graph grammar is sound, i.e., that all taxonomies produced by the grammar rules are correct, at least up to a certain size. We also show that the rules can generate all correct taxonomies up to a certain size (a completeness result).

Evidence-based lean conceptual data modelling languages

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. There is no known DL language that matches exactly the features of thoseprofiles and the common core is small (in the tractable DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.

CargO-S: A pattern-based well-founded legal domain ontology for the traceability of goods in logistic sea corridors

Building legal domain ontologies is a prominent challenge in the ontology engineering community. The ontology builders confront issues such as the complexity of the legal domain, the difficulty of applying existing ontology engineering approaches, and the intention of developing legal models faithful to realities. In this paper, we discuss constructing a well-founded legal domain ontology, named CargO-S, for the traceability of goods in logistic sea corridors. For building CargO-S, a pattern-oriented approach is applied, supported by ontology-driven conceptual modeling, ontology layering, and ontology reuse processes. CargO-S is grounded in the unified foundational ontology UFO by using the ontology-driven conceptual modeling language OntoUML. Besides, ontology layering is proposed to simplify the development process by dividing CargO-S into three layers located at different granularity levels: upper, core, and domain. For building the upper and core layers, conceptual ontology patterns are reused from the foundational ontology UFO and the legal core ontology UFO-L. These patterns are applied, either by extension or analogy with legal rules, for building the domain layer. CargO-S is then validated by implementing the ontology as OWL and SWRL rules. Finally, the performance and the semantic accuracy of CargO-S are evaluated using a dual evaluation approach.

Multi-level conceptual modeling: Theory, language and application

In many important subject domains, there are central real-world phenomena that span across multiple classification levels. In these subject domains, besides having the traditional type-level domain regularities (classes) that classify multiple concrete instances, we also have higher-order type-level regularities (metaclasses) that classify multiple instances that are themselves types. Multi-Level Modeling aims to address this technical challenge. Despite the advances in this area in the last decade, a number of requirements arising from representation needs in subject domains have not yet been addressed in current modeling approaches. In this paper, we address this issue by proposing an expressive multi-level conceptual modeling language (dubbed ML2). We follow a principled language engineering approach in the design of ML2, constructing its abstract syntax as to reflect a fully axiomatized theory for multi-level modeling (termed MLT*). We show that ML2 enables the expression of a number of multi-level modeling scenarios that cannot be currently expressed in the existing multi-level modeling languages. A textual syntax for ML2 is provided with an implementation in Xtext. We discuss how the formal theory influences the language in two aspects: (i) by providing rigorous justification for the language’s syntactic rules, which follow MLT* theorems and (ii) by forming the basis for model simulation and verification. We show that the language can reveal problems in multi-level taxonomic structures, using Wikidata fragments to demonstrate the language’s practical relevance.

Types and taxonomic structures in conceptual modeling: A novel ontological theory and engineering support

Types are fundamental for conceptual modeling and knowledge representation, being an essential construct in all major modeling languages in these fields. Despite that, from an ontological and cognitive point of view, there has been a lack of theoretical support for precisely defining a consensual view on types. As a consequence, there has been a lack of precise methodological support for users when choosing the best way to model general terms representing types that appear in a domain, and for building sound taxonomic structures involving them. For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO) - aimed at providing foundations for all major conceptual modeling constructs. At the core of this enterprise, there has been a theory of types specially designed to address these issues. This theory is ontologically well-founded, psychologically informed, and formally characterized. These results have led to the development of a Conceptual Modelling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed on conceptual model design in a variety of domains including academic, industrial, and governmental settings. These experiences exposed improvement opportunities for both the OntoUML language and its underlying theory, UFO. In this paper, we revise the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of OntoUML’s meta-types (e.g. kind, role, phase, mixin) should be considered not as restricted to substantial types but instead should be applied to model endurant types in general, including relator types, quality types, and mode types. We also contribute with a formal characterization of this fragment of the theory, which is then used to advance a new metamodel for OntoUML (termed OntoUML 2). To demonstrate that the benefits of this approach are extended beyond OntoUML, the proposed formal theory is then employed to support the definition of UFO-based lightweight Semantic Web ontologies with ontological constraint checking in OWL. Additionally, we report on empirical evidence from the literature, mainly from cognitive psychology but also from linguistics, supporting some of the key claims made by this theory. Finally, we propose a computational support for this updated metamodel.

Foundational ontologies, ontology‐driven conceptual modeling, and their multiple benefits to data mining

AbstractFor many years, the role played by domain knowledge in all stages of knowledge discovery has been recognized. However, the real‐world semantics embedded in data is often still not fully considered in traditional data mining methods. In this article, we argue that the quality of data mining results is directly related to the extent that they reflect important properties of real‐world entities represented therein. Analyzing and characterizing the nature of these entities is the very business of the area of formal ontology. We briefly elaborate on two particular types of artifacts produced by this area: foundational ontologies and ontology‐driven conceptual modeling languages grounded on them. We then elaborate on the benefits they can bring to several activities in a data mining process.This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Data Concepts

Improving Conceptual Modeling with Object-Process Methodology Stereotypes

As system complexity is on the rise, there is a growing need for standardized building blocks to increase the likelihood of systems’ success. Conceptual modeling is the primary activity required for engineering systems to be understood, designed, and managed. Modern modeling languages enable describing the requirements and design of systems in a formal yet understandable way. These languages use stereotypes to standardize, clarify the model semantics, and extend the meaning of model elements. An Internet of things (IoT) system serves as an example to show the significant contributions of stereotypes to model construction, comprehension, error reduction, and increased productivity during design, simulation, and combined hardware–software system execution. This research emphasizes stereotype features that are unique to Object-Process Methodology (OPM) ISO 19450, differentiating it from stereotypes in other conceptual modeling languages. We present the implementation of stereotypes in OPCloud, an OPM modeling software environment, explore stereotype-related problems, propose solutions, and discuss future enhancements.

Conceptual Software Engineering Applied to Movie Scripts and Stories

This study introduces another application of software engineering tools, conceptual modeling, which can be applied to other fields of research. One way to strengthen the relationship between software engineering and other fields is to develop a good way to perform conceptual modeling that is capable of addressing the peculiarities of these fields of study. This study concentrates on humanities and social sciences, which are usually considered softer and further away from abstractions and (abstract) machines. Specifically, we focus on conceptual modeling as a software engineering tool (e.g., UML) in the area of stories and movie scripts. Researchers in the humanities and social sciences might not use the same degree of formalization that engineers do, but they still find conceptual modeling useful. Current modeling techniques (e.g., UML) fail in this task because they are geared toward the creation of software systems. Similar Conceptual Modeling Language (e.g., ConML) has been proposed with the humanities and social sciences in mind and, as claimed, can be used to model anything. This study is a venture in this direction, where a software modeling technique, Thinging Machine (TM), is applied to movie scripts and stories. The paper presents a novel approach to developing diagrammatic static/dynamic models of movie scripts and stories. The TM model diagram serves as a neutral and independent representation for narrative discourse and can be used as a communication instrument among participants. The examples presented include examples from Propp s model of fairytales; the railway children and an actual movie script seem to point to the viability of the approach.

A retrospective on Telos as a metamodeling language for requirements engineering

Telos is a conceptual modeling language intended to capture software knowledge, such as software system requirements, domain knowledge, architectures, design decisions and more. To accomplish this, Telos was designed to be extensible in the sense that the concepts used to capture software knowledge can be defined in the language itself, instead of being built-in. This extensibility is accomplished through powerful metamodeling features, which proved very useful for interrelating heterogeneous models from requirements, model-driven software engineering, data integration, ontology engineering, cultural informatics and education. We trace the evolution of ideas and research results in the Telos project from its origins in the late eighties. Our account looks at the semantics of Telos, its various implementations and its applications. We also recount related research by other groups and the cross-influences of ideas thereof. We conclude with lessons learnt.

CONCEPTUAL MODELING LANGUAGE AGILA MODA

Modeling of data structures has always been an important topic in the discussions of software engineering practice. Recently, the idea of conceptual modeling has lost importance in these discussions. The fact that research in this area has not been pushed further a lot for the last decade can be considered as an evidence. However, this concept has great potential. Especially the idea of creating a paradigm agnostic model depicting facts of the real world –the so called “Universe of Discourse” –instead of concrete data structures following a certain logical data model makes it so powerful and valuable. Hence, it deserves further re-search to find best practices to utilize conceptual modeling effectively. The problems that discouraged software engineers from mak-ing use of conceptual modeling is that the models are hard to understand. Creating themis time-consuming, other stakeholders do not know what to do with them and creating the final data structures requires an additional process step. After all, it is mostly per-ceived as too expensive in time and money without creating an appropriate value.In this article, the existing approaches are exam-ined to find out their weaknesses and the reasons why they did not gain a broader acceptance. Therefore, the important requirements that a conceptual modeling language has to meet for practical fielding are determined. Furthermore, the concepts of semantic mod-eling languages are examined. Using semantics instead of mere structural discussions simplifies access and understanding for non-IT stakeholders. It helps to check the validity of the created data structures against the demands of the real business. In the further course, the concept of semantically irreducible sentence modeling will be discussed which can act as a bridge between semantic and conceptual modeling.With the results of these discussions, theconceptual modeling language AGILA MOD is presented. This model-ing language bases on the idea of depicting semantically irreducible sentences as graphical model. By this, it can act as a common platform all project participants can agree upon building thebridge between IT implementation and business requirements. The models can be created from semantically irreducible sentences and they can be read backwards into semantically irreducible sen-tences making this language easy to understand for all project participants. AGILA MOD is therefore intended to be as easy as possible to get started without a lot of learning efforts. Hence, it bases on the well-known Entity-Relationship language in a simpli-fied variant. A few additional constructs are added that also refer to well-known modeling techniques reducing the efforts of learning new elements nearly to zero.The derivation of AGILA MOD models to a logical model is done by following simple derivation rules making it less time consuming and hence less cost-intensive. This language shall act as a basis for further research targeting towards the new logical models of NoSQL as well as creating a comprehensive framework automating the derivation as much as possible. Additionally, the possibility of making use of polyglot persistence with this approach and the creation of a convenient API shall be considered in future research.

Applying the model-based concept framework to capturing the safety aspects of the suborbital human spaceflight missions

Safety issues are one of the core attributes of the projects of the New Space economy such as SpaceShipTwo and New Shepard. While these projects are on their way to shifting the paradigms in space tourism and transportation, the question of tourist safety remains in the cornerstone for a successful operations. One of the important questions we encounter when we start the development of complex systems such as the suborbital transportation and space tourism systems is: what are the concepts available and how can these concepts be represented using strictly defined ontology and model semantics? And finally – how this model-based information would support the system architect in keeping track of safety aspects? In this work we present a model-based concept framework that aims to address this issue. First a concept framework methodology is presented, after which we demonstrate its applicability to suborbital human spaceflight missions–SpaceShipTwo and New Shepard. The analytical conceptual difference between these concepts is demonstrated. The proposed framework includes: the information about the stakeholders and their needs; the solution-neutral environment (the problem statement) in which we formulate the functional intent; the solution-specific environment (solution statement) in which we see the possible solutions; the integrated concept revealed by means of the decomposition of such solution into internal elements and functions; and the concept of operations. Each one of these entries of the concept framework has a counterpart represented in conceptual modeling languages, such as Object-Process Methodology (OPM) or the System Modeling Language (SysML). Such a model-based concept framework encodes the core information required to define a suborbital tourism concept and represent it in a digital environment. We believe this will become a powerful tool to support the makers of architectural decisions that lead to concept and eventually to architecture, and that provides a safe operations for suborbital tourist services.

Frenemies: OPM and SysML Together in an MBSE Model

AbstractA Frenemy is “a person with whom one is friendly despite a fundamental dislike or rivalry.” (OED, 2018). The Systems Modeling Language (SysML) and the Object Process Methodology (OPM) are two such frenemies. OPM and SysML are different means of achieving Model‐Based Systems Engineering (MBSE), each with their own benefits, issues, supporters and detractors. The National Defense Industry Association (NDIA) defines MBSE as “an approach to engineering that uses models as an integral part of the technical baseline that includes the requirements, analysis, design, implementation, and verification of a capability, system, and/or product throughout the acquisition life cycle.” (NDIA, 2011) SysML is based on the Unified Modeling Language (UML) and includes diagrams that can be used to specify system requirements, behavior, structure and parametric relationships. SysML provides a means of defining high‐level abstract systems down to detailed physical systems. OPM is a “conceptual modeling language and methodology for capturing knowledge and designing systems. Based on a minimal universal ontology of stateful objects and processes that transform them, OPM can be used to formally specify the function, structure, and behavior of artificial and natural systems in a large variety of domains.” (ISO, 2015) OPM is used in some systems engineering graduate courses. Students graduating from these institutions are struggling to integrate the differing styles, philosophies, concepts and processes of SysML and OPM. A literature search reveals some papers that contrast SysML and OPM, but none that describe how the two can work together. This paper discusses a synergy of SysML and OPM in a SysML tool rather than promoting one language over another.