Figure 2 - uploaded by Guylerme Figueiredo
Content may be subject to copyright.
Source publication
In recent years, there has been a growth in the use of reference conceptual models, in general, and domain ontologies, in particular, to capture information about complex and critical domains. These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able...
Contexts in source publication
Context 1
... tessellate the possible space of objects in that domain, i.e., all objects belong to exactly one kind and do so necessar- ily. Typical examples of kinds include Person, Organization, Ship, and Harbor (see Figure 2). We can, however, have other static subdivisions (or subtypes) of a kind. ...
Context 2
... are naturally termed Subkinds. As an example, the kind 'Person' can be specialized in the subkinds 'Man' and 'Woman', likewise a kind 'Ship' can be specialized in the subkinds 'Cargo Ship' and 'Passenger Ship' (Figure 2). Object kinds and subkinds represent essential properties of objects (they are also termed rigid or static types [10]). ...
Context 3
... have, however, types that represent contingent or accidental properties of objects (termed anti-rigid types [10]). These include Phases (for example, in the way that 'being a living person' captures a cluster of contingent properties of a person, in the way that 'being a puppy' captures a cluster of contingent properties of a dog, or in the way that 'being an active harbor' captures contingent properties of a harbor, see Figure 2) and Roles (for example, in the way that 'being a husband' captures a cluster of contingent properties of a man, or that 'being a captain' captures contingent properties of a person, see Figure 2). The difference between the contingent properties represented by a phase and a role is the following: phases represent properties that are intrinsic to entities (e.g., 'being a puppy' is being a dog that is in a particular developmental phase; 'being a living person' is being a person who has the intrinsic property of being alive; 'being an active harbor' is being a harbor that is functional); roles, in contrast, represent properties that entities have in a relational context, i.e., con- tingent relational properties (e.g., 'being a husband' is to bear a number of commitments and claims towards a spouse in the scope of a marital relationship; 'being a student' is to bear a number of properties in the scope of an enrollment relationship with an educational institution; 'being a captain' is to bear a number of legal obligations and powers in the scope of a captain designation relationship to a ship, see Figure 2). ...
Context 4
... have, however, types that represent contingent or accidental properties of objects (termed anti-rigid types [10]). These include Phases (for example, in the way that 'being a living person' captures a cluster of contingent properties of a person, in the way that 'being a puppy' captures a cluster of contingent properties of a dog, or in the way that 'being an active harbor' captures contingent properties of a harbor, see Figure 2) and Roles (for example, in the way that 'being a husband' captures a cluster of contingent properties of a man, or that 'being a captain' captures contingent properties of a person, see Figure 2). The difference between the contingent properties represented by a phase and a role is the following: phases represent properties that are intrinsic to entities (e.g., 'being a puppy' is being a dog that is in a particular developmental phase; 'being a living person' is being a person who has the intrinsic property of being alive; 'being an active harbor' is being a harbor that is functional); roles, in contrast, represent properties that entities have in a relational context, i.e., con- tingent relational properties (e.g., 'being a husband' is to bear a number of commitments and claims towards a spouse in the scope of a marital relationship; 'being a student' is to bear a number of properties in the scope of an enrollment relationship with an educational institution; 'being a captain' is to bear a number of legal obligations and powers in the scope of a captain designation relationship to a ship, see Figure 2). ...
Context 5
... include Phases (for example, in the way that 'being a living person' captures a cluster of contingent properties of a person, in the way that 'being a puppy' captures a cluster of contingent properties of a dog, or in the way that 'being an active harbor' captures contingent properties of a harbor, see Figure 2) and Roles (for example, in the way that 'being a husband' captures a cluster of contingent properties of a man, or that 'being a captain' captures contingent properties of a person, see Figure 2). The difference between the contingent properties represented by a phase and a role is the following: phases represent properties that are intrinsic to entities (e.g., 'being a puppy' is being a dog that is in a particular developmental phase; 'being a living person' is being a person who has the intrinsic property of being alive; 'being an active harbor' is being a harbor that is functional); roles, in contrast, represent properties that entities have in a relational context, i.e., con- tingent relational properties (e.g., 'being a husband' is to bear a number of commitments and claims towards a spouse in the scope of a marital relationship; 'being a student' is to bear a number of properties in the scope of an enrollment relationship with an educational institution; 'being a captain' is to bear a number of legal obligations and powers in the scope of a captain designation relationship to a ship, see Figure 2). ...
Context 6
... (or relationships in a particular technical sense [8]) represent clusters of relational properties that "hang together" by a nexus (provided by a relator kind). Moreover, relators (e.g., marriages, enrollments, employments, presiden- tial mandates, citizenships, but also transportation contracts, trips, administration assignments and captain designations, see Figure 2) are full-fledged Endurants. In other words, entities that endure in time bearing their own essential and accidental properties and, hence, first-class entities that can change in a qualitative manner while maintaining their identity. ...
Context 7
... participate in relationships (relators) playing certain "roles". For instance, people play the role of spouse in a marriage relationship; a person plays the role of president in a presidential mandate; a harbor plays the role of a destination harbor in the scope of a trip, see Figure 2. 'Spouse' and 'President' (but also typically student, teacher, pet, destination harbor, captain, and traveling ship) are examples of what we technically term a role in UFO, i.e., a relational contingent sortal (since these roles can only be played by entities of a unique given kind). ...
Context 8
... example is the role 'Customer' (which can be played by both people and organizations). Another example is the role 'Ship Administrator' (which, again can be played by both people and organizations, see Figure 2). We call these role-like types that classify entities of multiple kinds RoleMixins. ...
Context 9
... result of filtering the model of Figure 1 according to this definition, produces a view containing only the fundamental kinds of things that exist in that domain, namely, people, ships, organizations and harbors (see Figure 2). This view is constituted by terminal symbols of the OntoUML pattern language as defined in [22], [27]. ...
Context 10
... result of filtering the model of Figure 1 according to this definition, produces a view that takes the two subkinds present in that model (PassengerShip and CargoShip) and produces a view that includes these subkinds and the classes and relations in this path until their (accidentally, in this case, common) kind is reached (see Figure 2). This view is constituted by instances of the Subkind Pattern of the OntoUML pattern language as defined in [22], [27]. ...
Context 11
... result of filtering the model of Figure 1 according to this definition, produces a view that takes the three phases present in that model (ExtinctHarbor, TemporarilyClosedHarbord and Active Harbor) and produces a view that includes these phases and the classes and relations in this path until their (acciden- tally, in this case, common) kind is reached (see Figure 2). This view is constituted by instances of the Phase Pattern of the OntoUML pattern language as defined in [22], [27]. ...
Context 12
... result of filtering the model of Figure 1 according to this definition, produces a view that takes the the roles present in that model (see Figure 1) and produces a view that includes these roles and the classes and relations in this path until their respective kind is reached (see Figure 2). This view is constituted by fragments that represent the core the Role Pattern of the OntoUML pattern language as defined in [22], [27]. ...
Context 13
... view includes all relator types in the model as well as the mediation relations connecting them to other types in the model. Taking the model of Figure 1 as an example, we have the RMV depicted in Figure 2. In this view, we have the relators Transportation Contracts (connecting Transportation Contract Clients and Ship Administrations), Ship Administration (connecting Ship Administrators and Ships), Captain Designation (connecting Captain and Ship) and Trip (connecting Departing Harbor, Destination Harbor and Traveling Ship). ...
Context 14
... any non-sortal in the model (rolemixin, mixin or category), the view should include: (i) this non-sortal and all its non-sortal supertypes, including these subtyping relations connecting them; (ii) the first sortal specializing this non-sortal as well as the patch from this sortal to the unique kind providing its identity principle [10]. Taking the model of Figure 1 as an example, we have the NSV depicted in Figure 2. In this view, we have, for instance, the rolemixin ShipAdministrator, the sortals that immediately specialize it (the roles Individual Administrator and Corporate Administrator) as well as the supertypes of each of these sortals that are in the path between them and their kinds (Person and Organization, respectively, in this case). ...
Context 15
... other words, by employing the explicitly defined MOF metamodel on which this editor is based, we have implemented algorithms to: automatically detect pattern occurrences, accessible through a detection dialog window (see example in Figure 3); extract views from OntoUML models comprising instances of these patterns. For instance, for the model of Figure 1, the tool will generate a structure of views that is equivalent to the one depicted in Figure 2. OntoUML is a pattern-driven modeling language. ...
Context 16
... chunks are themselves composed of even finer-grained chunks, namely, the aforementioned ontology design patterns. As one can observe in Figure 2, these resulting building blocks stay within the threshold of human-cognitive capacity and manipulation in short-term memory [19]. ...
Context 17
... one can observe in figure 2, the view extraction approach we propose here creates views that are composed of chunks derived from OntoUML ontology design patterns. In prelim- inary tests of our implementation against existing OntoUML models of different sizes and representing different domains, we observed that indeed the number of chunks within these views stay (in nearly the totally of cases) within the so- called Miller's Magic Number (7 ± 2 items) [19]. ...
Similar publications
In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns. This seems to be the case for machine learning algorithms too, but the linguistic and distributional reasons for this behaviour have not been investigated in depth so far. To tackle this issue, we show that the se...
In recent years, there has been a growth in the use of reference conceptual models, in general, and domain ontologies, in particular, to capture information about complex and critical domains. These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able...
In recent years, there has been a growth in the use of reference conceptual models, in general, and domain ontologies, in particular, to capture information about complex and critical domains. These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able...
Citations
... A number of approaches for complexity management have been proposed precisely for ODCML, e.g., [35,36]. In this paper, we refer to the task of producing a meaningful but reduced version of the original conceptual model by filtering out the details and keeping the most important notions -also known as summarizing or abstracting. ...
... There are prior works that have focused on managing the complexity of models and modeling languages. For instance, [20] defines a modularisation approach for large models. However, this article focuses on evaluating and improving user performance while understanding, updating, or creating ESEA method models, in the line of earlier work such as [21] and [22]. ...
Assessing business operations’ ethical, social, and environmental impacts is a key practice for establishing sustainable development. There is a multitude of methods that describes how to perform such assessments. Often these methods are supported by an ICT tool. In most cases, the tools are developed to support a single method only and do not allow any tailoring. Therefore, they are rigid and inflexible. In this article, we present a novel model-driven approach for alleviating managerial issues that arise as a consequence of the complex landscape of ethical, social, and environmental accounting methods and tools. We have developed an open-source, model-driven tool, called openESEA. OpenESEA parses and interprets textual models, that are specified according to a domain-specific language (DSL). We have performed another iteration of the DSL engineering process, which is in line with the design science paradigm. We have validated the new DSL version by means of a user study. As a result, we present a new version of the openESEA modeling language and interpreter. The results of the user study with regards to performance, perceived usefulness, and perceived ease of use of modeling language are encouraging and provide us with a basis to continue developing new versions with more functionalities. The contributions of this work include a new version of the modeling language, a new version of the interpreter, knowledge surrounding the development of these artifacts, and a protocol for evaluating the quality of textual DSLs. The modeling language and interpreter are relevant for sustainability practitioners and consultants since our tool support has the potential to reduce redundancy in ethical, social, and environmental accounting. Our work is valuable to researchers that aim to assess and reduce the complexity of their modeling languages.
... As SCRES is a complex domain that includes multiple dimensions of a supply chain (Adobor and McMullen, 2018), we identified model modularization as a possible technique to manage its complexity. This technique "consists of decomposing potentially large, monolithic ontologies into a set of smaller, interconnected components (modules)" (AbbËs et al., 2012) reducing complexity when the domain starts to grow (Figueiredo et al., 2018) and facilitating the understanding and knowledge interpretation by providing smaller subsets of an ontology (Khan and Keet, 2015). Additionally, Parent and Spaccapietra (2009) state that "it is very often impossible to divide a domain into disjoint subdomains" so links have to be defined between modules to express how the different sets of entities in the domain are related. ...
Purpose
This paper aims to propose a conceptualization of the supply chain resilience domain using conceptual modelling techniques formalized through a metamodel and illustrated through an example.
Design/methodology/approach
This research uses conceptual modelling techniques to build and modularize the metamodel, the latter to manage complexity. The metamodel was built iteratively and subsequently instantiated with an example of a yogurt factory to analyse its usefulness and theoretical relevance, and thus its contributions to the domain.
Findings
Conceptual modelling techniques can represent a complex domain such as supply chain resilience simply, and the proposed metamodel makes it possible to create models that become valuable decision support tools.
Originality/value
Consolidation and structuring of concepts in the supply chain resilience domain through conceptual modelling techniques.
... More recently, a number of approaches for complexity management have been proposed for Ontology-Driven CM languages -most notably OntoUML [9]by leveraging on the richer ontological semantics offered by these languages. These include [18,6,12]. The latter deals exactly with the topic of model abstraction and proposes a set of graph-rewriting rules for abstracting OntoUML patterns. ...
... In critical and complex scenarios, the number of concepts and axioms of a CM can grow significantly, leading to situations where "it is important that conceptual models are cognitively tractable" [6]. It is known that human working memory capacity in processing visual information is limited [15], and "displaying Abstracting Ontology-Driven Conceptual Models 3 2 Background ...
Ontology-driven conceptual models are widely used to capture information about complex and critical domains. Therefore, it is essential for these models to be comprehensible and cognitively tractable. Over the years, different techniques for complexity management in conceptual models have been suggested. Among these, a prominent strategy is model abstraction. This work extends an existing strategy for model abstraction of OntoUML models that proposes a set of graph-rewriting rules leveraging on the ontological semantics of that language. That original work, however, only addresses a set of the ontological notions covered in that language. We review and extend that rule set to cover more generally types of objects, aspects, events, and their parts.
... More recently, a number of approaches for complexity management have been proposed for Ontology-Driven CM languages -most notably OntoUML [9]by leveraging on the richer ontological semantics offered by these languages. These include [18,6,12]. The latter deals exactly with the topic of model abstraction and proposes a set of graph-rewriting rules for abstracting OntoUML patterns. ...
... In critical and complex scenarios, the number of concepts and axioms of a CM can grow significantly, leading to situations where "it is important that conceptual models are cognitively tractable" [6]. It is known that human working memory capacity in processing visual information is limited [15], and "displaying 2 Background ...
Ontology-driven conceptual models are widely used to capture information about complex and critical domains. Therefore, it is essential for these models to be comprehensible and cognitively tractable. Over the years, different techniques for complexity management in conceptual models have been suggested. Among these, a prominent strategy is model abstraction. This work extends an existing strategy for model abstraction of OntoUML models that proposes a set of graph-rewriting rules leveraging on the ontological semantics of that language. That original work, however, only addresses a set of the ontological notions covered in that language. We review and extend that rule set to cover more generally types of objects, aspects, events, and their parts.
... The proposal advanced here is, thus, aimed at conceptual models in business (organizational, social, and legal) domains, which form the bulk of the Information Systems discipline. For models that are centered on taxonomic relations (e.g., product types, biological taxonomies), we recommend alternative complexity management techniques, in particular, the static ontological views as proposed in [17]. In fact, this paper can be seen as a companion to [17] and [27] in a general research program of defining ontology-driven complexity management theories, techniques, and tools. ...
... For models that are centered on taxonomic relations (e.g., product types, biological taxonomies), we recommend alternative complexity management techniques, in particular, the static ontological views as proposed in [17]. In fact, this paper can be seen as a companion to [17] and [27] in a general research program of defining ontology-driven complexity management theories, techniques, and tools. While in these two papers the focus is on model recoding with ontology-design patterns, and on model abstraction, respectively, here we propose the notion of relationship-centric conceptual model modularization (or clustering). ...
... In this area, approaches like ArchiMate [34] define multiple viewpoints, which are archetypal "filters" over the complete model that select information over layers, concerns, and dimensions that are supposed to match particular aims (e.g., designing, deciding, informing), granularity (e.g., details, coherence, overview) and user roles (e.g., process manager, CEO, service designer, software developer, network administrator). However, unlike approaches for view extraction like [17], ArchiMate viewpoints are not designed to guarantee that the set of views is informationally equivalent to the original model. ...
In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, domain experts must be able to understand and reason with their content. In other words, these models need to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages on the rich semantics of ontology-driven conceptual models (ODCM). In particular, we propose a formal notion of Relational Context to guide the automated clusterization (or modular breakdown) of conceptual models. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator). The paper also presents computational support for automating the identification of Relational Contexts and this modular breakdown procedure. Finally, we report the results of an empirical study assessing the cognitive effectiveness of this approach.
... • ontouml2alloy: the Alloy transformation service is an update of the simulation service originally present in OLED [7]. The transformation generates the necessary files for model simulation through the Alloy Analyzer tool [18], now supporting OntoUML 2. • complexity management: the complexity management service offers algorithms to handle large-scale models, such as pattern-based view extraction [19], modularization based on relational contexts [17], and model abstraction [20]. These algorithms help modelers to organize and communicate large models in a cognitively tractable manner. ...
In the past decades, the Unified Foundational Ontology (UFO) has played an important role in supporting the development of ontologies in academic and business settings, being employed to represent widely diverse domains. In this period, a dedicated community of researchers has worked to support UFO and its representation language, OntoUML, by creating the OntoUML Lightweight Editor (OLED). Now that a new version of OntoUML is available, the need for up-to-date tool support has exposed the limitations of OLED, its development context, and the difficulties of bringing research contributions to the hands of modelers. To tackle these issues, this paper reflects upon the experiences of this community taking into consideration the goals of researchers (as developers) and modelers to devise a new microservice-oriented modeling infrastructure for OntoUML, called OntoUML as a Service (OaaS). This infrastructure supports future practical contributions to the language with a focus on lowering the entry barrier for the development of new contributions and enabling an easier deployment to modelers. The paper also discusses the details of implementing OaaS through a number of projects that currently implement this infrastructure.
... Of course, this requires much clarity in the representation of the involved concepts. Other future research topics include differentiating the roles of ontology [31]; conceptual modeling validation and learning; primitives [66], [67]; complexity management [25]; and semantic interoperability [32]. ...
Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research.
... It also, in line with [1], points at the fact that being a model (in the eyes of an observer) is a role of an artifact. 18 We should also realize that the observer observes the model (as artifact) as well, which therefore also creates a conception (in their mind) of the model. As a consequence, the observer needs to validate the alignment between their conception of the model and their conception of the domain, where the purpose of the model determines the alignment criteria. ...
... For the sake of this paper, we consider this as a case of a shared conception but not a model. 18 Authors such as [8], defend that although artifacts must be created by acts of intention, preexisting entities that are not artifacts can become constituents of artifacts via intentional acts of creation. For example, if one decides to use a pebble as a paper weight, then a new entity is created (that paper weight), which is then constituted by the original pebble. ...
... In the context of domain modeling, four important flavors of abstraction are [4]: (1) selection, where we decide to only consider certain elements and / or aspects of the domain; (2) classification; (3) generalization; and (4) aggregation. In our field of application, selection typically leads to frameworks of aspects / layers by which to model an enterprise, but also to mechanisms for view extraction, as well as clustering and model summarization [18,32]. Classification, typically leads to some class-instance and / or type-instance relationships, including type-instance relationships between types and higher-order types, i.e., multi-level structures [10]; generalization leads to the formation of specialization / generalization taxonomies, in which sub-types specialize properties of super-types; aggregation leads to the formation of partonomies of various kinds in which entities, seen as integral wholes, can be decomposed into parts. ...
The growing role of models across the life-cycle of enterprises, and their information and software systems, fuels the need for a more fundamental reflection on the foundations of modeling. Two of the core theories of the discipline of enterprise engineering (Factual Information (FI) theory and the Model Universe (MU) theory) aim at contributing to these foundations. The latest versions of the FI- and MU-theories have recently been published. Offering an analysis and criticism to them enables us to continue the important debate on the semiotic, ontological, and general philosophical foundations of domain modeling and enterprise modeling in particular. A core concept in the field of domain modeling is the conceptualization of the domain. In this paper, we specifically focus on the development of a deeper understanding of domain conceptualizations, while reflecting on the way this notion is positioned in the FI- and MU-theories.
... Another aspect related to which ontology-driven modeling languages can contribute to domain understanding is through their mechanism to support complexity management (Figueiredo, Duchardt, Hedblom, & Guizzardi, 2018;Guizzardi, Figueiredo, Hedblom, & Poels, 2019). Complex domains require representations that are both large in scale, and rich in subtleties. ...
For 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
Abstract
The quality of data mining results is directly related to the extent that they reflect important properties of real‐world entities represented therein.
