Cognition enhanced Natural language Information Analysis Method

Last updated

Cognition enhanced Natural language Information Analysis Method (CogNIAM) is a conceptual fact-based modelling method, that aims to integrate the different dimensions of knowledge: data, rules, processes and semantics. To represent these dimensions world standards SBVR, BPMN and DMN from the Object Management Group (OMG) are used. CogNIAM, a successor of NIAM, is based on the work of knowledge scientist. Caution, it is considered as pseudoscience Sjir Nijssen.[ citation needed ]

Contents

CogNIAM structures knowledge, gathered from people, documentation and software, by classifying it. For this purpose CogNIAM uses the so-called ‘Knowledge Triangle’. [1] The outcome of CogNIAM is independent of the person applying it. The resulting model allows the knowledge to be expressed in diagrammatic form as well as in controlled natural language. [2]

The different dimensions of knowledge

CogNIAM recognises 4 different dimensions of knowledge:

These dimensions influence each other heavily. Rules restrict data, Semantics describe the concepts and terms used in processes etc., therefore The aim of CogNIAM is to integrate these different dimensions.

Structuring knowledge

As mentioned earlier, CogNIAM classifies knowledge using the knowledge triangle . The knowledge that can be mapped to the knowledge triangle is structurally relevant and can be verbalised. Knowledge that cannot be verbalised, for example the ‘Mona Lisa’, is not included. Also the knowledge must be structurally relevant. Not structurally relevant is for example motivation (the why?). It is important information, but it is not an added value to the model. [3] The remaining knowledge can be mapped to the knowledge triangle. The knowledge triangle consists of three levels

Level 1 – The level of facts

The majority of knowledge consists of concrete facts. Facts describe possible current, past or future states. In CogNIAM a fact is defined as “a proposition taken to be true by a relevant community”. [4]

An example of a level 1 fact is:

“The capital of Italy is Rome.”
Level 2 – The domain specific level

In this level the rules that govern the facts of level 1 are specified. For the example above a rule governing the level 1 facts could be “a country has exactly one capital”. This is a rule that ensures no untrue states or disallowed transitions between different states can occur at level 1. Besides rules level 2 contains six more knowledge categories, which are discussed in the next chapter.

Level 3 – The generic level

This level is not associated to any specific domain, it says nothing about capitals or countries. As level 2 governs the facts on level 1, the generic level governs the knowledge categories of level 2. It consists of the same knowledge categories, but here they are applied to the content of level 2. In other words, level 3 contains the rules that determine the rules. The generic level can also be seen as a domain specific level with the domain being ‘domain specific knowledge’. As a result, level 3 also governs itself.

Knowledge categories

Level 2 and 3 of the knowledge triangle consist of seven knowledge categories:

  1. Concept definitions describe the meaning of every term or group of terms at the fact level. A large part of the semantics dimension can be found here.
  2. Fact types provide the functionality to define which kinds of facts are considered to be within the scope of the domain of interest.
  3. Communication patterns:
    1. Fact communication patterns act as a communication mechanism to be used as a template to communicate facts using terms the subject matter expert is familiar with
    2. Rule communication patterns act as communication mechanism for the rules (see below) of the conceptual schema.
  4. Rules, distinguishing between:
    1. Integrity or validation rules, also known as constraints, restrict the set of facts and the transitions between the permitted sets of facts to those that are considered useful. In terms of data quality, integrity rules are used to guarantee the quality of the facts.
    2. Derivation rules are used to derive or calculate new information (facts) based on existing information.
    3. Exchange rules transfer facts into the administration of that domain or remove facts from the administration. In other words, they specify how facts are added and/or removed from the fact base so that the system stays in sync with the communication about the outside world.
    4. Event rules specify when to update the set of ground facts by a derivation rule or exchange rule in the context of a process description.
  5. Process descriptions specify the fact consuming and/or fact generating activities (the exchange and/or derivation rules) to be performed by the different actors for that process, as well as the event rules invoking the execution of those exchange and derivation rules in an ordered manner.
  6. Actors, identifying the involved participants and their responsibilities in the processes (in terms of the exchange and derivation rules they need to execute).
  7. Services, identifying the realisations of the process descriptions in terms of information products to be delivered or consulted

Related Research Articles

Semantics is the study of reference, meaning, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics and computer science.

<span class="mw-page-title-main">Data model</span> Model that organizes elements of data and how they relate to one another and to real-world entities.

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

A business rule defines or constrains some aspect of business. It may be expressed to specify an action to be taken when certain conditions are true or may be phrased so it can only resolve to either true or false. Business rules are intended to assert business structure or to control or influence the behavior of the business. Business rules describe the operations, definitions and constraints that apply to an organization. Business rules can apply to people, processes, corporate behavior and computing systems in an organization, and are put in place to help the organization achieve its goals. For example, a business rule might state that no credit check is to be performed on return customers. Other examples of business rules include requiring a rental agent to disallow a rental tenant if their credit rating is too low, or requiring company agents to use a list of preferred suppliers and supply schedules. While a business rule may be informal or even unwritten, documenting the rules clearly and making sure that they don't conflict is a valuable activity. When carefully managed, rules can be used to help the organization to better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty.

A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure.

<span class="mw-page-title-main">IDEF</span> Family of modeling languages

IDEF, initially an abbreviation of ICAM Definition and renamed in 1999 as Integration Definition, is a family of modeling languages in the field of systems and software engineering. They cover a wide range of uses from functional modeling to data, simulation, object-oriented analysis and design, and knowledge acquisition. These definition languages were developed under funding from U.S. Air Force and, although still most commonly used by them and other military and United States Department of Defense (DoD) agencies, are in the public domain.

<span class="mw-page-title-main">Object-role modeling</span> Programming technique

Object-role modeling (ORM) is used to model the semantics of a universe of discourse. ORM is often used for data modeling and software engineering.

<span class="mw-page-title-main">Information model</span>

An information model in software engineering is a representation of concepts and the relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse. Typically it specifies relations between kinds of things, but may also include relations with individual things. It can provide sharable, stable, and organized structure of information requirements or knowledge for the domain context.

Cognitive semantics is part of the cognitive linguistics movement. Semantics is the study of linguistic meaning. Cognitive semantics holds that language is part of a more general human cognitive ability, and can therefore only describe the world as people conceive of it. It is implicit that different linguistic communities conceive of simple things and processes in the world differently, not necessarily some difference between a person's conceptual world and the real world.

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, physical models are physical object such as a toy model that may be assembled and made to work like the object it represents.

A modeling perspective in information systems is a particular way to represent pre-selected aspects of a system. Any perspective has a different focus, conceptualization, dedication and visualization of what the model is representing.

<span class="mw-page-title-main">IDEF1X</span>

Integration DEFinition for information modeling (IDEF1X) is a data modeling language for the development of semantic data models. IDEF1X is used to produce a graphical information model which represents the structure and semantics of information within an environment or system.

In information science, an upper ontology is an ontology which consists of very general terms that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

Constraint logic programming is a form of constraint programming, in which logic programming is extended to include concepts from constraint satisfaction. A constraint logic program is a logic program that contains constraints in the body of clauses. An example of a clause including a constraint is A(X,Y):-X+Y>0,B(X),C(Y). In this clause, X+Y>0 is a constraint; A(X,Y), B(X), and C(Y) are literals as in regular logic programming. This clause states one condition under which the statement A(X,Y) holds: X+Y is greater than zero and both B(X) and C(Y) are true.

Fully Communication Oriented Information Modeling (FCO-IM) is a method for building conceptual information models. Such models can then be automatically transformed into entity-relationship models (ERM), Unified Modeling Language (UML), relational or dimensional models with the FCO-IM Bridge toolset, and it is possible to generate complete end-user applications from them with the IMAGine toolset. Both toolsets were developed by the Research and Competence Group Data Architectures & Metadata Management of the HAN University of Applied Sciences in Arnhem, the Netherlands.

Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.

The Semantics of Business Vocabulary and Business Rules (SBVR) is an adopted standard of the Object Management Group (OMG) intended to be the basis for formal and detailed natural language declarative description of a complex entity, such as a business. SBVR is intended to formalize complex compliance rules, such as operational rules for an enterprise, security policy, standard compliance, or regulatory compliance rules. Such formal vocabularies and rules can be interpreted and used by computer systems. SBVR is an integral part of the OMG's model-driven architecture (MDA).

<span class="mw-page-title-main">Information</span> Facts provided or learned about something or someone

Information is an abstract concept that refers to that which has the power to inform. At the most fundamental level information pertains to the interpretation of that which may be sensed. Any natural process that is not completely random, and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analog signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation.

Gerardus Maria "Sjir" Nijssen is a Dutch computer scientist, former professor of computer science at the University of Queensland, consultant, and author. Nijssen is considered the founder of verbalization in computer science, and one of the founders of business modeling and information analysis based on natural language.

A semantic decision table uses modern ontology engineering technologies to enhance traditional a decision table. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab in 2006. A semantic decision table is a set of decision tables properly annotated with an ontology. It provides a means to capture and examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner.

Frame-based terminology is a cognitive approach to terminology developed by Pamela Faber and colleagues at the University of Granada. One of its basic premises is that the conceptualization of any specialized domain is goal-oriented, and depends to a certain degree on the task to be accomplished. Since a major problem in modeling any domain is the fact that languages can reflect different conceptualizations and construals, texts as well as specialized knowledge resources are used to extract a set of domain concepts. Language structure is also analyzed to obtain an inventory of conceptual relations to structure these concepts.

References

  1. ↑ Sjir Nijssen and André Le Cat. Kennis Gebaseerd Werken], 2009. p. 118-148
  2. ↑ Nijssen, Gerardus Maria, and Terence Aidan Halpin. Conceptual Schema and Relational Database Design: a fact oriented approach. Prentice-Hall, Inc., 1989.
  3. ↑ Sjir Nijssen. Kenniskunde 1A. 2001. p. 3
  4. ↑ Lemmens, Inge, Jean-Paul Koster, and Serge Valera. "Achieving interoperability at semantic level." OTM Confederated International Conferences" On the Move to Meaningful Internet Systems. Springer International Publishing, 2015. p. 210
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.