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===== Content ===== | ===== Content ===== | ||

- | | + | The OntoDT ontology defines: |

- | The OntoDT sub-ontology defines: | + | * datatype characterizing operation and a taxonomy of datatype characterizing operations, |

- | * datatype characterising operation and a taxonomy of datatype characterizing operations, | + | |

* datatype quality and a taxonomy of datatype qualities | * datatype quality and a taxonomy of datatype qualities | ||

* a datatype taxonomy comprising of classes and instances of | * a datatype taxonomy comprising of classes and instances of | ||

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* defined datatypes. | * defined datatypes. | ||

+ | ==== Datatype and value space ==== | ||

+ | In the OntoDT ontology, the //datatype// class is modeled as a subclass of the //OBI: data representational model// class. It defines the type of data, with the set of distinct values that the data can take, the properties of those values, and the operations on those values. The //datatype// class is represented with the //has-member// relation to the //value space specification// class and the //has-operation// relation to the //characterizing operation// class. In addition, OntoDT models //datatype properties// as subclasses of the //quality// class and connects them using the //has-quality// relation. | ||

+ | |||

+ | The //value space specification// class is modeled in OntoDT as a subclass of the //OntoDM: specification entity// class. It specifies the collection of values for a given datatype. The value space of a given datatype can be defined in different ways: by enumerating the values; with axioms using a set of fundamental notions; as a subset of values defined in another value space with a given set of properties; or as a combination of arbitrary values from some other defined value space by specifying a construction procedure. | ||

+ | |||

+ | ==== Characterizing operations ==== | ||

+ | A //characterizing operation// is defined as //IAO: directive information entity// that specifies those operations on the datatype that distinguish it from other datatypes having identical value spaces. The characterizing operation of a datatype can be: //niliadic, monadic, dyadic// and //n-adic//. | ||

+ | * A //niliadic operation// specifies an operation that yields values of a given datatype. | ||

+ | * A //monadic operation// specifies an operation that maps a value of a given datatype into a value of the given datatype, or into a value of the //boolean// datatype. | ||

+ | * A //dyadic operation// specifies an operation that maps a pair of values of a given datatype into a value of the given datatype, or into a value of the //boolean// datatype. | ||

+ | * An //n-adic operation// specifies an operation that maps an ordered n-tuple of values (n>2), each of which is of a specific datatype, into values of a given datatype. | ||

+ | Finally, all characterizing operation classes have defined subclasses, which represent datatype specific operations. | ||

+ | |||

+ | ==== Datatype properties ==== | ||

+ | A //datatype property// is defined as a //quality// that specifies the intrinsic properties of the data units represented by the datatype, regardless of the properties of their representations in computer systems. Each datatype has a set of unique datatype properties. These include property classes such as: //order//, //numericalness//, //cardinality//, //exactness// ,//equality// , and //boundedness//. | ||

+ | * //Order// is a datatype property that denotes whether there exists an order relation defined on its value space. | ||

+ | * //Numericalness// denotes whether the values in the value space are quantities expressed in a mathematical numbering system. | ||

+ | * //Cardinality// denotes the notion of cardinality of the value space. | ||

+ | * //Exactness// denotes whether every value from the value space is distinguishable from every other value in the value space. | ||

+ | * //Boundedness// is a property that denotes the boundaries of the value space. | ||

+ | All datatype property classes have defined subclasses. For example, the //boundedness// class has the following subclasses: //bounded// (//bounded below, bounded above//) and //unbounded// (//unbounded below, unbounded above//). | ||

+ | |||

+ | ==== Extended datatype ==== | ||

+ | In OntoDT, an //extended datatype// (named //'subtype'// in the ISO standard) is defined as a //IAO: data representational model// that is derived from an existing datatype by restricting the value space to a subset of the base datatype, while maintaining all operations. The base type denotes the role of a datatype as a //parametric datatype// on which a generator operates to produce a new datatype. An extended datatype is defined by a //subtype generator// that represents the relationship between the value spaces of the base type and the extended datatype. | ||

+ | |||

+ | In OntoDT, we define the following classes of subtype generators: //range generator//, //selection generator//, //exclusion generator//, //size generator//, //extension generator//, and //explicit subtype generator//. Subtype generators can change the set of datatype properties valid for the base datatype, and this is the reason we do not represent them simply as subclasses of the datatype class. For example, applying the range generator to an unbound datatype will make it bounded. | ||

+ | |||

+ | Using these notions, we can represent an extended datatype of any previously defined type. For example, by using a range subtype generator we can place a new upper and/or lower bound on the value space of a chosen base datatype. The //positive integer// datatype is a extended datatype of the //integer datatype// obtained by limiting the value space with a lower bound of zero. | ||

+ | |||

+ | Using these notions, we can represent an extended datatype of any previously defined type. For example, by using a range subtype generator we can place a new upper and/or lower bound on the value space of a chosen base datatype. The //positive integer// datatype is a extended datatype of the //integer datatype// obtained by limiting the value space with a lower bound of zero. | ||

===== Versions and Download ===== | ===== Versions and Download ===== | ||

==== Release version 1 ==== | ==== Release version 1 ==== | ||

- | [[http://kt.ijs.si/panovp/OntoDM/OntoDT.owl | + | * The main ontology file [[http://ontodm.com/ontodt/OntoDT.owl|OntoDT.owl]] |

- | ]] | + | * Extension for representing BioXSD datatypes [[http://ontodm.com/ontodt/OntoDT-BioTypes.owl|OntoDT-BioTypes.owl]] |

+ | * Annotation and querying machine learning datasets use case | ||

+ | * annotations of datasets [[http://ontodm.com/ontodt/OntoDTinstances.xls|OntoDTinstances.xls]] | ||

+ | * OWL representation of the dataset anonations [[http://ontodm.com/ontodt/OntoDTinstances.owl|OntoDTinstances.owl]] | ||

+ | * extension of OntoDT and OntoDM-core for annotating datasets [[http://ontodm.com/ontodt/clus-instances.owl|clus-instances.owl]] | ||

===== Publications ===== | ===== Publications ===== | ||

- | Panče Panov. [[https://www.dropbox.com/s/0w1gwjja76sipgi/PanovPhD2012.pdf|A Modular Ontology of Data Mining]]. Doctoral Thesis. Jožef Stefan International Postgraduate School. 2012 (Chapter 5 OntoDT: Ontology Module for Datatypes) | + | * Panče Panov, Larisa Soldatova, Sašo Džeroski. [[http://www.sciencedirect.com/science/article/pii/S0020025515005800|Generic Ontology of Datatypes]]. Information Sciences 329 (2016) 900–920 |

+ | * Panče Panov. [[https://www.dropbox.com/s/0w1gwjja76sipgi/PanovPhD2012.pdf|A Modular Ontology of Data Mining]]. Doctoral Thesis. Jožef Stefan International Postgraduate School. 2012 (Chapter 5 OntoDT: Ontology Module for Datatypes) | ||

===== OntoDT@Bioportal ===== | ===== OntoDT@Bioportal ===== | ||

[[http://bioportal.bioontology.org/ontologies/1588]] | [[http://bioportal.bioontology.org/ontologies/1588]] |