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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...
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... 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. ...
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... 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]). ...
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... 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). ...
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... 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). ...
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... 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). ...
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... (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. ...
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... 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). ...
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... 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. ...
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... 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]. ...
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... 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]. ...
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... 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]. ...
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... 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]. ...
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... 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). ...
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... 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). ...
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... 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. ...
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... 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]. ...
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... 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]. ...
Citations
... As such, listeners have mostly been left to infer the speaker's intended meaning by using prior utterances, context about the speaker, objects, and concepts [5], [6]. Whilst visual short-term memory bottlenecks and issues with complicated reference in conceptual models are caused by the human ability for processing unknown information, these issues are not unique to humans [7], [8]. The listener's familiarity with the speaker is primarily determined by the circumstance, event, and topic expertise. ...
Depending on the social features of the speaker and the social setting in which they are speaking, the relationship between meaning and context might alter. In order to interpret the meaning from dysarthric speech, this paper proposes a theoretical framework for employing speech-event representations, also known as situational projections. The multi-layered approach has been broken down into four main components: a few-shot learner that builds up speaker familiarity; a situational projection component that marshals natural sentences and the built-up familiarity markers into a vector triple; a contextualizer that builds up ontological concepts of the input triple; and finally, a transducer that assumes the function of a logical listener.
... As an illustration of interpretability of models for enterprise actors we present two examples of the same enterprise case, one made with UFO and one with PFO. UFO in combination with the language OntoUML, published in [16], is shown in Fig. 6. The UFO model contains 25 concepts as 25 entities related to each other with 29 relations. ...
Models are fundamental to enterprise information systems design. In recent years information systems which are generated from models, such as conceptual models, has emerged among researchers as well as in practice. We present Phenomenological Foundational Ontology for ontology driven conceptual modeling, with the purpose of improving information quality and manageability of information systems generated from models. The ontology is outlined and its application is exemplified using an easy-to-use web based tool which generates runtime systems including user interface, with the capability to process and communicate data.
Conceptual models need to be comprehensible and maintainable by humans to exploit their full value in faithfully representing a subject domain. Modularization, i.e. breaking down the monolithic model into smaller, comprehensible chunks has proven very valuable to maintain this value even for very large models. The quality of modularization however often depends on application-specific requirements, the domain, and the modeling language. A well-defined generic modularizing framework applicable to different modeling languages and requirements is lacking. In this paper, we present a customizable and generic multi-objective conceptual models modularization framework. The multi-objective aspect supports addressing heterogeneous requirements while the framework’s genericity supports modularization for arbitrary modeling languages and its customizability is provided by adopting the modularization configuration up to the level of using user-defined heuristics. Our approach applies genetic algorithms to search for a set of optimal solutions. In this paper, we present the details of our Generic Genetic Modularization Framework with a case study to show i) the feasibility of our approach by modularizing models from multiple modeling languages, ii) the customizability by using different objectives for the modularization quality, and, finally, iii) a comparative performance evaluation of our approach on a dataset of ER and ECore models.