Wiki Termontography

Termontography is a multidisciplinary approach in which theories and methods for a multilingual terminological analysis of sociocognitive terminology theory are combined with methods and guidelines for ontology engineering. A clear distinction is made between conceptual modeling at a culture-independent level and a culture-specific analysis of units of understanding. Hence, the prototypical structuring of understanding is taken into consideration.

The newly coined term ‘termontography’ could be interpreted as the field composed of

  1. Terminology: refers to the usage and study of terms, that is to say words and compound words generally used in specific contexts (WikiPedia).
  2. Ontology: the study of the categories of things that exist or may exist in some domain (John F. Sowa: http://www.jfsowa.com/ontology/).
  3. Terminography: compiling collections of the vocabulary of special languages (Pointer: http://www.computing.surrey.ac.uk/AI/pointer/report/section1.html).
Purposes of the termontography approach:
  1. Describing ontologies with (multilingual) terminological information.
  2. Structuring terminological resources with ontologies.

Termontography tools The CFE is a tool for the construction of a Categorization Framework (CF). A CF is a language independent concept model that can be used to classify terminological information, and that describes a certain domain by means of concepts and concept relations. The TW is an application for the compilation of a (multilingual) domain specific corpus. This corpus may then be used to extract terminological information that can be linked to the CF. The output of this process will be a termontological resource, i.e. an ontologically structured terminological database, that can be used in specialized applications (for example the KBExplorer). The TE is an application to manage termontological resources. Terminological information, i.e. terms and term descriptions, can be added and classified using the CF. The CF can also be transformed into a concept model consisting of concepts and concept relations. These concepts and concept relations can then be linked to (multilingual) terminology.

Categorization Framework (CF)

A CF consists of the following items: term, category, meta category, term meaning, meaning, attribute, property, bi-directional relation and bi-directional relation instance.

For instance, the text string main classified by the French language category fr constitutes the French term main. The English language category en, for instance, belongs to the meta category language. If we add the category fr, for instance, to the meta category language, this category represents the French language. With this definition a term meaning now refers to a certain meaning, while it still can be represented by a single term. The term meaning that references the English term joint and the category marijuana cigarette with meta category cigarette, for instance, clearly describes this term meaning as a marijuana cigarette (see figure 1). The term meaning wind instrument, for instance, could reference a certain meaning with references to the meta category wind instrument and the category wind instrument. The meta category wind instrument then indicates that categories such as flute and trombone, which belong to this meta category, are wind instruments. The category wind instrument, however, could be used to classify something, for instance a bottle of beer, as a wind instrument. The meta category thus specifies the superordinate concept of a category, while the category is used to indicate a certain aspect of the classified CF item.

To further clarify the intended meaning of a concept relation (or concept), we allow properties to be added to CF items (categories and term meanings, for example). To implement properties, we introduce the items attribute and property.

For example, the attribute description with value type text string could be used to describe a term meaning, while the attribute extra information with value type URL could be used to refer to a web page with extra information about a category. For example, we could add a property with attribute extra information and value http://en.wikipedia.org/wiki/Knee to the category knee.

To efficiently manage (meta) categories, relations are used. A relation between two CF items has two directions, mostly with a different meaning. Therefore, we introduce the item bi-directional relation to specify a bi-directional relation between two CF items.

A relation can be created by adding a category with meta category relation. We may add the categories is part of and has part, for example, to the meta category relation. A logical bi-directional relation should reference both these two opposing categories. Let us notate this bi-directional relation as ((is part of), (has part)). The bi-directional relation instance that references the bi-directional relation ((is part of), (has part)), the source category lower arm and the target category arm would indicate that a lower arm is part of an arm.

Figure 1: CF items (italic) in relation to their corresponding semiotic notions (bold).


Almost brilliant! To show true brilliance, try saying it again in little words my (very bright) six year old can understand.

Sometimes a cigar is just an Object.


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