Semantic System Model

A Semantic System Model is a system model and knowledge representation that helps create abstract representations that support understanding tasks and semantic interpretations.

• AKA: Conceptual Framework, Conceptualization, Conceptual Schema, Semantic Representation, Meaning Model.
• Context:
  • It can typically contain Semantic Model Concept Records through semantic model structures and semantic model hierarchies.
  • It can typically represent Semantic Model Relations through semantic model concept interactions and semantic model association rules.
  • It can typically enable Semantic Model Machine Understanding through semantic model concept formalization and semantic model logic encoding.
  • It can typically support Semantic Model Reasoning Processes through semantic model logical frameworks and semantic model inference rules.
  • It can typically guide Semantic Model Knowledge Organization through semantic model domain representations and semantic model taxonomy structures.
  • It can typically express Semantic Model Meaning Constraints through semantic model formal specifications and semantic model consistency rules.
  • It can typically capture Semantic Model Domain Knowledge through semantic model concept definitions and semantic model relationship encodings.
  • ...
  • It can often provide Semantic Model Shared Understanding through semantic model common conceptualizations and semantic model standard vocabularies.
  • It can often improve Semantic Model System Interoperability through semantic model ontological alignments and semantic model mapping rules.
  • It can often support Semantic Model Inference Tasks through semantic model reasoning mechanisms and semantic model deduction rules.
  • It can often facilitate Semantic Model Knowledge Transfer through semantic model conceptual mappings and semantic model translation functions.
  • It can often enable Semantic Model Knowledge Discovery through semantic model pattern recognition and semantic model relationship mining.
  • It can often preserve Semantic Model Domain Expertise through semantic model expert knowledge encoding and semantic model best practice capture.
  • ...
  • It can range from being an Informal Semantic Model to being a Formal Semantic Model, depending on its semantic model formalization level.
  • It can range from being a Domain-Specific Semantic Model to being a General Knowledge Semantic Model, depending on its semantic model knowledge scope.
  • It can range from being a Rule-Based Semantic Model to being a Statistical Semantic Model, depending on its semantic model representation approach.
  • It can range from being a Static Semantic Model to being a Dynamic Semantic Model, depending on its semantic model evolution capability.
  • It can range from being a Lightweight Semantic Model to being a Heavyweight Semantic Model, depending on its semantic model expressiveness level.
  • It can range from being a Human-Oriented Semantic Model to being a Machine-Oriented Semantic Model, depending on its semantic model target audience.
  • ...
  • It can be produced by a Semantic Model Creation System with semantic model modeling capabilitys.
  • It can abide by some Semantic Modeling Methodology.
  • It can have Semantic Model Verification Methods through semantic model logical consistency checks.
  • It can utilize Semantic Model Languages for semantic model formal expression.
  • It can integrate with Semantic Model Tools for semantic model construction tasks.
  • It can support Semantic Model Querying through semantic model access interfaces.
  • ...
• Example(s):
  • Knowledge Representation Semantic Models, such as:
    • Ontologies, such as:
      • Domain Ontology for semantic model domain knowledge representation.
      • Upper Ontology for semantic model general concept organization.
      • Application Ontology for semantic model specific system support.
    • Knowledge Bases, such as:
      • Semantic Knowledge Base for semantic model structured information storage.
      • Ontological Knowledge Base for semantic model formal knowledge representation.
      • Expert System Knowledge Base for semantic model rule-based reasoning.
    • Semantic Networks, such as:
      • Conceptual Graph for semantic model visual concept relationship.
      • Topic Map for semantic model information organization.
      • Knowledge Graph for semantic model entity relationship mapping.
  • Domain-Specific Semantic Models, such as:
    • Business Domain Semantic Models, such as:
      • Enterprise Data Model for semantic model organizational understanding.
      • Business Process Model for semantic model workflow representation.
      • Product Ontology for semantic model product classification.
    • Scientific Domain Semantic Models, such as:
      • Biological Ontology for semantic model organism classification.
      • Chemical Ontology for semantic model molecular structure.
      • Physics Model for semantic model phenomenon representation.
    • Technical Domain Semantic Models, such as:
      • Software Architecture Model for semantic model system specification.
      • Database Schema for semantic model data organization.
      • Service Model for semantic model capability description.
  • Cognitive Semantic Models, such as:
    • Mental Models, such as:
      • Mental Model of Self for semantic model self-conception.
      • User Mental Model for semantic model system interaction understanding.
      • Shared Mental Model for semantic model team coordination.
    • Conceptual Models, such as:
      • Scientific Conceptual Model for semantic model theory representation.
      • Design Conceptual Model for semantic model solution planning.
      • Learning Conceptual Model for semantic model knowledge acquisition.
  • Implementation-Specific Semantic Models, such as:
    • Contract AI CDM for semantic model contract AI system.
    • Lexical Semantic Mapping Model for semantic model language processing.
    • Semantic Web Model for semantic model web resource description.
    • Semantic Database Model for semantic model data relationship.
  • Formal Logic Semantic Models, such as:
    • Description Logic Model for semantic model formal reasoning.
    • First-Order Logic Model for semantic model predicate representation.
    • Modal Logic Model for semantic model possibility expression.
  • ...
• Counter-Example(s):
  • Physical Models, which are tangible representations rather than abstract semantic models.
  • Simulation Models, which emphasize dynamic execution rather than semantic model static representation.
  • Logical Data Models, which focus on implementation structure rather than semantic model meaning.
  • Raw Data Structures, which lack semantic interpretation unlike semantic models.
  • Statistical Models without semantics, which capture numerical patterns rather than semantic model meaning.
  • Syntactic Models, which represent structural form rather than semantic model content.
• See: Knowledge Model, Domain Model, Ontological Framework, Mental Model, AI Capability Boundary, Concept, Knowledge Representation, Semantic Relation, Formal Model, Conceptual Structure, Semantic Information, Semantic Parsing Task, Knowledge Base, Paradigm.

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References

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Conceptual_model Retrieved:2023-7-23.
  • A conceptual model is a representation of a system. It consists of concepts used to help people know, understand, or simulate a subject the model represents. In contrast, a physical model focuses on a physical object such as a toy model that may be assembled and made to work like the object it represents.

    The term may refer to models that are formed after a conceptualization or generalization process. Conceptual models are often abstractions of things in the real world, whether physical or social. Semantic studies are relevant to various stages of concept formation. Semantics is basically about concepts, the meaning that thinking beings give to various elements of their experience.

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Conceptual_model#Conceptual_model_vs Retrieved:2023-7-23.
  • Conceptual Models and semantic models have many similarities, however the way they are presented, the level of flexibility and the use are different.

    Conceptual models have a certain purpose in mind, hence the core semantic concepts are predefined in a so-called meta model. This enables a pragmatic modelling but reduces the flexibility, as only the predefined semantic concepts can be used. Samples are flow charts for process behaviour or organisational structure for tree behaviour.

    Semantic models are more flexible and open, and therefore more difficult to model. Potentially any semantic concept can be defined, hence the modelling support is very generic. Samples are terminologies, taxonomies or ontologies.

    In a concept model each concept has a unique and distinguishable graphical representation, whereas semantic concepts are by default the same.

    In a concept model each concept has predefined properties that can be populated, whereas semantic concepts are related to concepts that are interpreted as properties.

    In a concept model operational semantic can be built-in, like the processing of a sequence, whereas a semantic model needs explicit semantic definition of the sequence.

    The decision if a concept model or a semantic model is used, depends therefore on the "object under survey", the intended goal, the necessary flexibility as well as how the model is interpreted. In case of human-interpretation there may be a focus on graphical concept models, in case of machine interpretation there may be the focus on semantic models.

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Conceptual_schema Retrieved:2015-8-4.
  • A conceptual schema is a high-level description of a business's informational needs. It typically includes only the main concepts and the main relationships among them. Typically this is a first-cut model, with insufficient detail to build an actual database. ...

2013

• http://en.wikipedia.org/wiki/Conceptual_model
  • In the most general sense, a model is anything used in any way to represent anything else. Some models are physical objects, for instance, a toy model which may be assembled, and may even be made to work like the object it represents. Whereas, a conceptual model is a model that exists only in the mind. Conceptual models are used to help us know and understand the subject matter they represent.

    The term conceptual model may be used to refer to models which are formed after a conceptualization process in the mind. ...

2013

• Wikipedia http://en.wikipedia.org/wiki/Conceptual_model_(computer_science)
  • 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 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 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.

2009

• (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=conceptualization
  • S: (n) conceptualization, conceptualisation, formulation (inventing or contriving an idea or explanation and formulating it mentally)
  • S: (n) conceptualization, conceptualisation, conceptuality (an elaborated concept)
• http://en.wiktionary.org/wiki/conceptualization
  • 1. the act of conceptualizing, or something conceptualized
• (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=conceptualize
  • S: (v) gestate, conceive, conceptualize, conceptualise (have the idea for) "He conceived of a robot that would help paralyzed patients"; "This library was well conceived"
• http://en.wiktionary.org/wiki/conceptualize#Verb
  • 1. To interpret a phenomenon by forming a concept
  • 2. To conceive the idea for something

2007

• (Obitko, 2007) ⇒ Marek Obitko. (2007). “Translations between Ontologies in Multi-Agent Systems", Ph.D. dissertation, Faculty of Electrical Engineering, Czech Technical University in Prague. http://obitko.com/tutorials/ontologies-semantic-web/specification-of-conceptualization.html
  • A conceptualization can be defined as an intensional semantic structure that encodes implicit knowledge constraining the structure of a piece of a domain. Ontology is a (partial) specification of this structure, i.e., it is usually a logical theory that expresses the conceptualization explicitly in some language. Conceptualization is language independent, while ontology is language dependent. The use can be illustrated in the figure below - it shows how an ontology restricts (i.e., defines) possible use of constructs used in the description of the domain. Notice that ontology does not have to express all the possible constraints - the level of details in conceptualization depends on the requirements of the intended application and expressing conceptualization in ontology in addition depends on the used ontology language.

1993

• (Gruber, 1993) ⇒ Tom Gruber. (1993). “A translation approach to portable ontology specifications." Knowledge Acquisition, 2(5):199--220.
  • A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly. An ontology is an explicit specification of a conceptualization..

1983

• (Norman, 1983) ⇒ Donald A. Norman. (1983). “Some Observations on Mental Models." Mental models 7, no. 112
  • … A conceptual model is invented to provide an appropriate representation of the target system, appropriate in the sense of being accurate, consistent, and complete. …

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