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A mental model captures ideas in a problem domain, while a conceptual model represents 'concepts' (entities) and relationships between them.
A conceptual model in the field of computer science is a special case of a general conceptual model. To distinguish from other types of models, it is also known as a domain model. Conceptual modeling should not be confused with other modeling disciplines such as data modelling, logical modelling and physical modelling. The conceptual model is explicitly chosen to be independent of design or implementation concerns, for example, concurrency or data storage. The aim of a conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts. The conceptual model attempts to clarify the meaning of various, usually ambiguous terms, and ensure that problems with different interpretations of the terms and concepts cannot occur. Such differing interpretations could easily cause confusion amongst stakeholders, especially those responsible for designing and implementing a solution, where the conceptual model provides a key artifact of business understanding and clarity. Once the domain concepts have been modeled, the model becomes a stable basis for subsequent development of applications in the domain. The concepts of the conceptual model can be mapped into physical design or implementation constructs using either manual or automated code generation approaches. The realization of conceptual models of many domains can be combined to a coherent platform.
A conceptual model can be described using various notations, such as UML, ORM or OMT for object modelling, or IE or IDEF1X for Entity Relationship Modelling. In UML notation, the conceptual model is often described with a class diagram in which classes represent concepts, associations represent relationships between concepts and role types of an association represent role types taken by instances of the modelled concepts in various situations. In ER notation, the conceptual model is described with an ER Diagram in which entities represent concepts, cardinality and optionality represent relationships between concepts. Regardless of the notation used, it is important not to compromise the richness and clarity of the business meaning depicted in the conceptual model by expressing it directly in a form influenced by design or implementation concerns.
This is often used for defining different processes in a particular company or institute.
Ontology Engineering with Diagrams
Ontology engineering in computer science and information science is a field which studies the methods and methodologies for building ontologies: formal representations of a set of concepts within a domain and the relationships between those concepts. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.
Ontology engineering aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the inter-operability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies for a particular domain. ”
Automated processing of information not interpretable by software agents can be improved by adding rich semantics to the corresponding resources, such as video files. One of the approaches for the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide structured data in, or based on, RDF, RDFS, and OWL. Ontology engineering is the design and creation of such ontologies, which can contain more than just the list of terms (controlled vocabulary); they contain terminological, assertional, and relational axioms to define concepts (classes), individuals, and roles (properties) (TBox, ABox, and RBox, respectively). Ontology engineering is a relatively new field of study concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. A common way to provide the logical underpinning of ontologies is to formalize the axioms with description logics, which can then be translated to any serialization of RDF, such as RDF/XML or Turtle. Beyond the description logic axioms, ontologies might also contain SWRL rules. The concept definitions can be mapped to any kind of resource or resource segment in RDF, such as images, videos, and regions of interest, to annotate objects, persons, etc., and interlink them with related resources across knowledge bases, ontologies, and LOD datasets. This information, based on human experience and knowledge, is valuable for reasoners for the automated interpretation of sophisticated and ambiguous contents, such as the visual content of multimedia resources. Application areas of ontology-based reasoning include, but are not limited to, information retrieval, automated scene interpretation, and knowledge discovery.
Process philosophy (also ontology of becoming, processism, or philosophy of organism) identifies metaphysical reality with change and development. Since the time of Plato and Aristotle, philosophers have posited true reality as "timeless", based on permanent substances, while processes are denied or subordinated to timeless substances. If Socrates changes, becoming sick, Socrates is still the same (the substance of Socrates being the same), and change (his sickness) only glides over his substance: change is accidental, whereas the substance is essential. Therefore, classic ontology denies any full reality to change, which is conceived as only accidental and not essential. This classical ontology is what made knowledge and a theory of knowledge possible, as it was thought that a science of something in becoming was an impossible feat to achieve.
In opposition to the classical model of change as accidental (as argued by Aristotle) or illusory, process philosophy regards change as the cornerstone of reality—the cornerstone of Being thought of as Becoming. Philosophers who appeal to process rather than substance include Heraclitus, Karl Marx, Friedrich Nietzsche, Henri Bergson, Martin Heidegger, Charles Sanders Peirce, William James, Alfred North Whitehead, Alfred Korzybski, R. G. Collingwood, Alan Watts, Robert M. Pirsig, Charles Hartshorne, Arran Gare, Nicholas Rescher, Colin Wilson, and Gilles Deleuze. In physics Ilya Prigogine distinguishes between the "physics of being" and the "physics of becoming". Process philosophy covers not just scientific intuitions and experiences, but can be used as a conceptual bridge to facilitate discussions among religion, philosophy, and science.
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