Table of content:
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Table of content:
Note that each Wiki page has its own table of content about the details on that page.
Using Gellish can start in two ways:
1. Starting by expressing information in Gellish and
2. Starting by building software that can produce or read and interpret Gellish expressions.
Understanding the first is required for starting the second. Therefore this section gives guidance on how to start making Gellish expressions. At the same time it describes a number of basic principles that are applied in Gellish.
The easiest way to start is by copying a Gellish expression format table from an existing table. For example, by copying one of the tables that are available in the download area or from the tables available in the Gellish project on GitHub. Such a table can be extended or simplified according to the options that are described in the document 'Gellish Syntax and Contextual Facts', which is summarized in this wiki. Another option is to copy the full Gellish expression format table definition from the Expr_Table_Def.py module source code software, written in Python on GitHub.
A core principle of the Gellish language is the observation that any idea can be expressed as a collection of one or more binary relations, being relations between two objects. This is the ORO principle: the Object-Relation_type-Object principle (where 'Object' is interpreted in its widest sense). That principle is supported by the observation that simple as well as complex ideas can be expressed as collections of binary relations, while taking into account that complex ideas are objects in their own right. For example, activities and processes are 'objects' that are interactions between multiple objects, whereas those interactions can be modeled by a collection of binary involvement relations, each of which expresses the role that an object plays, or the way in which it is involved, in the activity or process.
Thus expressing information in Gellish begins with chopping the information in binary relations, each relating two objects, and selecting the appropriate kind of relation.
|Name of left hand object||Name of the kind of relation||Name of right hand object|
|House-1||has as part||Kitchen-1|
|The Eiffel tower||is located in||Paris|
|Paris||is classified as a||city|
Searching for the proper kinds of relations in the various expressions implies searching the Gellish Dictionary for kinds of relations and their subtypes and the phrases by which they are denoted. Therefore it is advisable that users shall have access to a tool that enables searching the Dictionary. There are a large number of standard kinds of relations defined in the Gellish language. Those kinds of relations form the basic grammar of Gellish and the fact that they are standardized make that software can build on their meaning. This makes the language computer interpretable and system independent and it enables making the software reusable. The number of kinds of relations and other concepts is not limited to the current dictionary, because the Gellish Modeling Methodology includes a specification of how the dictionary can be extended with additional concepts and relation types. For example, if the Eiffel tower or Paris or an appropriate relation type would not yet exist in the Gellish English Dictionary, then they can be added in a Domain Dictionary or as proprietary extensions. All the Gellish expressions can be stored and exchanged using the standard Gellish Expression format.
Furthermore, it is recognized that in a binary relation each of the related objects plays a particular role of a particular kind. Therefore, an extended expression of a fact in Gellish includes those (kinds of) roles and thus becomes: Object-First_role-Relation_type-Second_role-Object. For example, the fact that ‘the Eiffel tower is located in Paris’ is expressed as relation between the Eiffel tower and Paris, which relation is classified by the standard Gellish relation type called <is located in>. Furthermore, the Eiffel tower plays the role of located and Paris plays the role of location in that relation.
The principle that each idea can be expressed as a collection of binary relations is shared with e.g. the NIAM and the Object Role Modeling (ORM) methods. A difference between NIAM, ORM and Gellish is that Gellish classifies the relation and distinguishes the relation from the two roles of the related objects, whereas ORM only recognizes the two roles and does recognize the relation as a separate object only when it is 'objectified'. Furthermore, a more important difference is that neither NIAM nor ORM define standard kinds of roles nor kinds of relations not standard kinds of things. Thus they do not include a dictionary and do not define a user language.
Conventional methods for data modeling leave it in the freedom of developers to create their own entity types, attribute types or object types and relation types and to allocate their own names to them. Developers usually put constraints on the (names of) allowed values that may be entered as 'instances' when the data models are populated by data entry in the systems. However, those allowed values are usually not coordinated across multiple systems, so that their content does not comply with some standardized and formalized language.
This implies that those methods in fact encourage everybody to create his or her own language (dictionary as well as grammar). This is the root cause of the difficulties with exchanging data between systems and with integrating data that originate from different databases.
This means that knowledge, requirements or definitions of kinds of things shall be expressed as relations between concepts that are already present in the taxonomic dictionary or those concepts shall be added by defining them as subtypes of existing concepts.
For example, assume that the concept 'capital' is required and is not yet included in the dictionary. Then the concept is defined by a specialization relation (denoted by the phrase 'is a kind of'), followed by a textual definition as follows:
|Name of left hand object||Name of the kind of relation||Name of right hand object||Partial textual definition|
|capital||is a kind of||role||of a city when it …|
|capital||is by definition a role of a||city|
This common use of the same concept definitions and vocabulary, together with a proper management of unique identifiers (which is discussed later), enable that collections of Gellish expressions from different sources can be integrated without conversion. It also enables that data that originate from one system can be generated or interpreted by any other system that is Gellish enabled.
Furthermore, in addition to the common use of the same dictionary, every user can create his own individual objects (conventionally referred to as 'instances') and whenever concepts that are needed by user are missing in the Gellish Dictionary it is recommended to create proprietary subtype kinds of objects or kinds of relations. Users are also recommended to give feedback by proposing definitions of new concepts as extensions of the formal Gellish Dictionary, so that more concepts definitions become shared between Gellish users.
Thus Gellish English is not just a modeling method, like e.g. NIAM, ORM, ER and other methods, but it is a complete formal language, including a normal, although enhanced, electronic English (taxonomic) dictionary.
Another core concept of Gellish is that every individual thing (or instance) that is used in Gellish expressions needs to be classified by an explicit classification relation with an existing concept (kind of thing or class) in the Gellish Dictionary or by a concept that is defined as a proprietary extension of that dictionary. If it is required for such a classification to define a proprietary extension of the Gellish dictionary, then such a new concept should be added according to the rules for a proper definition, which includes defining the concept as a subtype of an existing supertype concept (as is described above).
This concept for Gellish expressions differs from the requirements in conventional databases. In conventional databases every instance is implicitly classified by the definition of the attribute type (the definition of a database table column) of which it is an instance. Thus, the kinds of instances that can be stored in a conventional database is limited by the (fixed) number of entity types and attribute types or object types that are defined in its data model. In Gellish the explicit classification implies that anything can be stored in a Gellish Database, because every concept in the Gellish dictionary can be used to classify an instance, whereas missing concepts can be added without modifying the database. This means that Gellish is equivalent to an unlimited large data model.
The structure of Gellish expressions (its syntax) is intended to enable the expression of ideas of any kind. It was discovered that it is possible to express ideas as collections of binary relations, whereas such relations follows always a basic semantic pattern. As a consequence there appears to be a generic structure that is suitable for expressing ideas of any kind. This means that every expression has basically the same generic structure, although there are a large number of optional elements within that structure. Furthermore, it appears to be possible to model that structure in a single table with a number of optional columns. As a result, the Gellish Expression format consists of only one precisely defined table. The core of the structure of that table is illustrated by the examples in Table 1.
|Name of left hand object||Name of the kind of relation||Role of the right hand object||Name of right hand object||Unit of measure|
|the Eiffel tower||is classified as a||tower|
|Paris||is classified as a||city|
|the Eiffel tower||is located in||Paris|
|tower||can have as aspect a||height|
|Paris||is a part of||France|
|the Eiffel tower||has as aspect a||the height of the Eiffel tower||height|
|the height of the Eiffel tower||has on scale a value approximately equal to||300||m|
Table 1, Core of the Gellish Expression format with example expressions of ideas
Each line in Table 1 is the expression of a single idea by a binary relation or a combination of binary relations. By default the expressions are interpreted as assertions (statements). Each related object plays a particular role or kind of role in a relation of the specified kind. That role or kind of role is determined by the definition of the kind of relation and may be further defined by the related objects. In most cases the roles and kinds of roles can remain implicit. For example, in Table 1, the Eiffel tower and Paris are objects that play different roles in the relations of the various kinds.
For example the Eiffel tower and Paris play respectively the (implicit) roles 'located' and 'location' in the third relation. That relation is classified by a kind that is denoted by the phrase ‘is located in’. The Gellish language contains proper definitions of standard kinds of relations, such as 'is located in'. Those definitions of kinds of relations include the proper definition of those kinds of roles as well as what kind of things may play roles of such kinds. These definitions enable software to some extent to execute semantic verification of the semantic correctness of the expressions.
The lines that classify ‘Eiffel tower’ as a ‘tower’ and ‘Paris’ as a ‘city’, are examples of classification relations, which relate individual things to known concepts (kinds of things) in the Gellish dictionary. Those classification relations add those individual things as new concepts to the dictionary.
Note that tower and city should be standard concepts that exist already in the Gellish English Dictionary.
The line that specifies that the Eiffel tower <has as aspect a> height, called 'the height of the Eiffel tower' shows the use of an additional column in the table to introduce the intrinsic aspect. It should be noted that this statement can be expressed without the need to also have the statement on the fourth line that states that a tower can have a height (meaning that it can have a value for a height in an information model; in reality it has by definition some height). The language definition does not put any constraints on which object can have which kind of aspect.
On the last line the (intrinsic) aspect is quantified on a scale by an approximate value, whereas the relation is classified not only as an approximate equality on a scale, but the relation is also classified by the quantification method, being the quantification on a meter scale.
Note that the usage of separate lines for quantification of aspects enables to allocate different values for different moments in time, and in different units or different measurement accuracies or by constraining the quantification by various kinds of relations, such as greater than, less than, greater than or equal, etc.
The Gellish expression format defines UIDs for the above names of concepts and defines a number of other contextual binary relations that can be represented in additional column on the same line in the standard table. For example about the author, date of creation, etc. See for more details the appropriate page in this wiki. The format is defined in the document The Gellish Syntax and Contextual Facts. The definition of the syntax/format allows for the usage of user defined subsets of binary relations and thus of subsets of columns in expression format tables. It also does not prescribe a particular encoding, although Unicode (utf-8) and CSV or JSON are recommended.
Note that Gellish does not prescribe the use of the Gellish Expression format. The separate binary relations that are combine on one line in such a table can also be expressed in other formats, such as RDF or XML.
The various kinds of relationships are standardized in the Gellish Dictionary and the kinds of relations are the core elements that determine the expression power of the formal language. Expression in Gellish are only correct Gellish expressions if they use kinds of relations that are selected from the Gellish Taxonomic Dictionary. The kinds of relations form a specialization hierarchy (subtype-supertype hierarchy) of kinds of relations to ensure consistency of the language. That hierarchy enables that software can also search for expressions that use more specialized subtypes of kinds of relations for expressing meanings more precisely. For example, software can automatically search for things that are connected to each other by any kind of connection, but it can also search for welded connections only. The kinds of relations are defined further in the dictionary (ontology) by the specification of the kinds of roles that they require. Those roles are also explicitly defined and arranged in a specialization hierarchy that is compliant with the hierarchy of the kinds of relations. Finally it is defined which kind of objects can play roles of those kinds, whereas those role players are also defined and arranged in a specialization hierarchy. Together these definitions and hierarchies enable Gellish powered software to verify the use of kinds of relations in Gellish expressions is consistent with the definition of the formal language and it enables the application of logic reasoning during the search to answer questions and queries.
Some examples of important binary kinds of relations with rather trivial kinds of roles are:
The kinds of roles determine the kinds of things that are suitable to play those kinds of roles, because only specific kinds of things can play specific kinds of roles.
It is important to note that the natural language phrase representing the kind of relationship determines which kind of role acts as the first role and which kind of role acts as the second role. For example, assume there is an idea that can be expressed in Gellish by the assembly relation on the first row in Table 2, then that expression is equivalent to the expression of the idea on the second row in Table 2 and actually expresses the same idea, but in an inverse expression. According to the above normal English convention both expressions imply that object A has a role as part and object B has a role as whole in a relation that is classified as an assembly relation which is also called a ‘part-whole’ relation.
|UID of idea||Name of left hand object||Name of kind of relation||Name of right hand object|
|1||A||is a part of||B|
|1||B||has as a part||A|
Table 2, Roles of objects in relations and in inverse expressions.
The most generic kind of relationship in Gellish English is simply called ‘relation’ or ‘might be related to’. That concept forms the top of the subtype-supertype hierarchy (taxonomy) of kinds of relations. The definition of that ‘relation’ concept is defined in Gellish by expressions of as many pairs of elementary facts as the order of the relation type. As an example, the first four rows of Table 3 show Gellish expressions that define the concept 'relation' by the expression of two pairs of elementary facts. Note, by the way, that we use the terms relation and relationship as synonyms.
|UID of idea||Name of left hand object||Name of relation type||Name of right hand object|
|1||relation||has by definition as first role a||relator|
|2||relator||can be played by a||anything|
|3||relation||has by definition as second role a||related|
|4||related||can be played by a||anything|
|5||is a part of||is a base phrase for||assembly relation|
|6||has as a part||is an inverse phrase for||assembly relation|
Table 3, Definition of a kind of relation
The example of the definition of a kind of relation on the first four lines in Table 3 is representative for the definition of any kind of relation, provided that each concept in such a definition is itself also defined by a subtype-supertype relation with its supertype concept(s) in which also a textual definition is provided. This in the above example, the concepts relation, relator, related all shall be defined as being a kind of some supertype kind.
The Gellish dictionary contains terms as well as synonyms, abbreviations, codes, etc. for names of concepts as well as for names of kinds of relationships. Each term and each alias is defined to belong to the vocabulary of a natural language and within that to a language community such as a discipline, a standard or a particular organization. A special 'language' is called 'international', being reserved for terms that are used internationally, such as units of measure, chemical formulas, codes, etc. Users can define their own terminology as aliases that are preferred terms for their organization. In the latter case the organization should be specified as the 'language community' where the term has its base.
Kinds of relations have names, but are usually denoted by natural language phrases, such as the phrases in the column 'name of kind of relation' in Table 3. Note that the phrases have a particular reading direction, which means that the left hand and the right hand object have different roles in the relation. In order to make expressions in which the left and right hand objects are switched, Gellish also defined inverse phrases. Whether a phrase or an inverse phrase is used (with switched related objects) has no impact on the meaning of an expression. Phrases and inverse phrases are aliases for the names of kinds of relations The way in which the synonym phrases are defined for the relation types in the Gellish dictionary is illustrated on row 5 in Table 3. Row 6 of Table 3 shows an example of the definition of an inverse phrase for denoting the kind of relation with the name assembly relation.
Translations can be defined in the same way as aliases, but for the definition of bulk translations, the Gellish Expression format allows for additional columns that are dedicated for terms in a particular language. Such columns have column IDs that are the UIDs of those languages.
Conventional data modeling methods separate meta models from instances. In fact those methods use two distinct languages: one for the specification of the meta model (such languages are called data modeling languages), the data model defines concepts such as entity types and attribute types with their names as the vocabulary of the data model language. The data model of the application domain acts as a framework for the interpretation of another language, being the terminology in the language of the users.
In Gellish such a separation does not exist. Gellish can be used for both kinds of modeling activities: data or knowledge modeling as well as creating information models of individual things (instance models). The only difference between those two applications of Gellish is that relations between kinds require other kinds of relations than relations between individual things or relations between individual things and kinds. The above Table 1 and 2 provide examples of relations between individual things, where the classification relations cross the bridge between the world of individual things and the world of the kinds of things (the Dictionary). Table 3 and 4 illustrate kinds of relations that are used for expressing ideas about kinds of things. For example, relations that are used for the description of functions. Table 4 illustrates the usage of Gellish phrases for relations that express an idea that states that an engine has by definition an ability to drive (a pump) and can be performer of a process of driving a pump, in which process a pump is a subject.
|Unique fact ID||Name of left hand object||Name of relation type||Name of right hand object||Description|
|1||engine||has by definition as aspect a||ability to drive||with roles possessor and possessed|
|2||engine||can be a performer of a||driving a pump||with roles performer and performed|
|3||pump||can be a subject in a||driving a pump||with roles subject and subjecting|
Table 4, Relations used to describe functions
Further relation types are defined in the part of the Gellish Taxonomic Dictionary that contains the collection of expressions in the base upper ontology.