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OntoDM-core - Ontology of Core Data Mining Entities

Background

In data mining, the data used for analysis are organized in the form of a dataset. Every dataset consists of data examples. The task of data mining is to produce some type of a generalization from a given dataset. Generalization is a broad term that denotes the output of a data mining algorithm. A data mining algorithm is an algorithm, that is implemented as computer program and is designed to solve a data mining task. Data mining algorithms are computer programs and when executed they take as input a dataset and give as output a generalization.

In this context, the OntoDM-core sub-ontology formalizes the key data mining entities needed for the representation of mining structured data in the context of a general framework for data mining (Dzeroski, 2006).

Design

OntoDM-core is expressed in OWL-DL , a de facto standard for representing ontologies. The ontology is being developed using the Protege ontology editor. The ontology is freely available at this page and at BioPortal.

In order to ensure the extensibility and interoperability of OntoDM-core with other resources, in particular with biomedical applications, the OntoDM-core ontology follows the Open Bio-Ontologies (OBO) Foundry design principles, such as the

  • use of an upper-level ontology,
  • the use of formal ontology relations,
  • single inheritance, and
  • the re-use of already existing ontological resources where possible.

The application of these design principles enables cross-domain reasoning, facilitates wide re-usability of the developed ontology, and avoids duplication of ontology development efforts. Consequently, OntoDM-core imports the upper-level classes from the BFO version 1.1 and formal relations from the OBO Relational Ontology and an extended set of RO relations.

Following best practices in ontology development, the OntoDM-core ontology reuses appropriate classes from a set of ontologies, that act as mid-level ontologies for OntoDM-core. These include the

For representing the mining of structured data, we import the OntoDT ontology of datatypes. Classes that are referenced and reused in OntoDM-core are imported into the ontology by using the Minimum Information to Reference an External Ontology Term (MIREOT) principle and extracted using the OntoFox web service.

Ontology Structure

For the domain of DM, we propose a horizontal description structure that includes three layers:

  • a specification layer,
  • an implementation layer, and
  • an application layer.

Having all three layers represented separately in the ontology will facilitate different uses of the ontology. For example, the specification layer can be used to reason about data mining algorithms; the implementation layer can be used for search over implementations of data mining algorithms and to compare various implementations; and the application layer can be used for searching through executions of data mining algorithms.

This description structure is based on the use of the upper-level ontology BFO and the extensive reuse of classes from the mid-level ontologies OBI and IAO. The proposed three layer description structure is orthogonal to the vertical ontology architecture which comprises an:

  • upper-level,
  • a mid-level, and
  • a domain level.

This means that each vertical level contains all three description layers.

Layers

The specification layer contains BFO: generically dependent continuants at the upper-level, and IAO: information content entities at the mid-level. In the domain of data mining, example classes are data mining task and data mining algorithm.

The implementation layer describes BFO: specifically dependent continuants, such as BFO: realizable entities (entities that are executable in a process). At the domain level, this layer contains classes that describe the implementations of algorithms.

The application layer contains classes that aim at representing processes, e.g., extensions of BFO: processual entity. Examples of (planned) process entities in the domain of data mining are the execution of a data mining algorithm and the application of a generalization on new data, among others.

Relations between layers

The entities in each layer are connected using general relations, that are layer independent, and layer specific relations. Examples of general relations are is-a and part-of: they can only be used to relate entities from the same description layer. For example, an information entity (member of the specification layer) can not have as parts processual entities (members of the application layer). Layer specific relations can be used only with entities from a specific layer. For example, the relation precedes is only used to relate two processual entities. The description layers are connected using cross-layer relations. An entity from the specification layer is-concretized-as an entity from the implementation layer. Next, an implementation entity is-realized-by an application entity. Finally, an application entity, e.g., a planned process achieves-planned-objective, which is a specification entity.

Key OntoDM-core classes

The ontology includes the representation of the following entities: data specification and dataset, data mining task, generalization, data mining algorithm, constraints and constraint based data mining tasks and algorithms, and data mining scenario.

Data

The main ingredient in the process of data mining is the data. In OntoDM-core, we model the data with a data specification entity that describes the datatype of the underlying data. For this purpose, we import the mechanism for representing arbitrarily complex datatypes from OntoDT ontology.

Descriptive and output data specification

In OntoDM-core, we distinguish between a descriptive data specification, that specifies the data used for descriptive purposes (e.g., in the clustering and pattern discovery), and output data specification, that specifies the data used for output purposes (e.g., classes/targets in predictive modeling). A tuple of primitives or a graph with boolean edges and discrete nodes are examples of data specified only by a descriptive specification. Feature-based data with primitive output and feature-based data with structured output are examples of data specified by both descriptive and output specifications.

Dataset

OntoDM-core imports the IAO class dataset (defined as `a data item that is an aggregate of other data items of the same type that have something in common') and extends it by further specifying that a DM dataset has part data examples.

OntoDM-core also defines the class dataset specification to enable reasoning about data and datasets. It specifies the type of the dataset based on the type of data it contains. Using data specifications and the taxonomy of datatypes from the OntoDT ontology, in OntoDM-core we build a taxonomy of datasets.

Versions and Download

Release version 1

Papers

Panov P., Soldatova L., Džeroski S. Ontology of core data mining entities. Data Mining and Knowledge Discovery 28(5-6):1222-1265, 2014 DOI 10.1007/s10618-014-0363-0


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