2016 GenericOntologyofDatatypes

(Panov et al., 2016) ⇒ Panče Panov, Larisa N. Soldatova, and Sašo Džeroski. (2016). “Generic Ontology of Datatypes.” In: Information Sciences — Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, 329(C). doi:10.1016/j.ins.2015.08.006

Subject Headings: Datatype, Data Structure, ChEBI.

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Abstract

We present OntoDT, a generic ontology for the representation of scientific knowledge about datatypes. OntoDT defines basic entities, such as datatype, properties of datatypes, specifications, characterizing operations, and a datatype taxonomy. We demonstrate the utility of OntoDT on several use cases. OntoDT was used within an Ontology of core data mining entities for constructing taxonomies of datasets, data mining tasks, generalizations and data mining algorithms. Furthermore, we show how OntoDT can be used to annotate and query dataset repositories. We also show how OntoDT can improve the representation of datatypes in the BioXSD exchange format for basic bio-informatics types of data. The generic nature of OntoDT enables it to support a wide range of other applications, especially in combination with other domain specific ontologies: the construction of data mining workflows, annotation of software and algorithms, semantic annotation of scientific articles, etc. OntoDT is open source and is available at http://www.ontodt.com.

1. Introduction

Data processing is at the heart of science. Scientific research workflows rely heavily on datatype representations. Especially in data mining research it is impossible to efficiently (semi-) automatically connect parts of workflows, such as data preprocessing and data mining, perform analysis of the research results and communicate the research outputs, without machine processable representation of datatypes and their properties. There is a need for a standardized semantically-defined and machine amenable representation of scientific datatypes to support cross-domain applications. Unfortunately, the existing representations of datatypes do not fully address such a need.

In the literature, there exist different definitions of datatypes. In computer science, a datatype is usually defined as a “classification that identifies various types of data, such as boolean, integer, discrete and others, that determines the possible values for that type, operations on the values of the data, and the way the values of that type can be stored” [56]. Nell and Walker [8] discuss the difference]] between a data structure and datatype in the sense that “data structure refers to the study of data and how to represent data objects within a program; that is, the implementation of structured relationships” while a datatype defines “the properties of classes of objects in addition to how these objects might be represented in a program”. Martin [36] also discusses the difference between data structures and datatypes and states that “depending on the point of view, a data object is characterized by its type (for the user) or by its structure (for the implementer) ”.

In this paper, we present OntoDT, a generic ontology of datatypes. OntoDT defines the semantics, i.e., meaning of the key entities and represents the knowledge about datatypes in a machine friendly way. The OntoDT ontology is based on the latest revised version of the ISO / IEC 11404 standard for datatypes [ 23 ].

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This paper is organized as follows. In Section 2, we present the background related to the development of the OntoDT ontology. In Section 3, we review and discuss the related work. Next, in Section 4, we present the ontology design principles and implementation, and in Section 5 we present the key OntoDT classes. In Section 6, we present the OntoDT datatype taxonomy. Finally, we present the ontology evaluation (Section 7), and three use cases of the ontology (Section 8). We conclude the paper with a discussion (Section 9) and a summary of contributions and points for further work (Section 10).

2. Background

The OntoDT development started within the frame of an ontology for data mining (OntoDM) [ 44 ]. The main idea of using a formalized description of datatypes for the domain of data mining was to characterize the types of data contained in a dataset, the applicability of a data mining task on data from a given datatype, and the applicability of a data mining algorithm on a dataset. Due to generality and reuse purposes, OntoDT has evolved to become an independent ontology.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 GenericOntologyofDatatypes	Sašo Džeroski Larisa Soldatova Panče Panov			Generic Ontology of Datatypes				10.1016/j.ins.2015.08.006		2016