Representing Entities in the OntoDM Data Mining Ontology

Panov, Panče; Džeroski, Sašo; Soldatova, Larisa

doi:10.1007/978-1-4419-7738-0_2

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…The paper goes far beyond our earlier work on OntoDM (Panov et al 2008(Panov et al , 2010. The preliminary versions of the ontology reported in the conference paper and in the book chapter contain 165 and 280 classes accordingly, while the OntoDM-core ontology described in this paper has 856 classes.…”

Section: Introductionmentioning

confidence: 89%

“…In 2011, there has been an attempt to establish a Data Mining Ontologies (DMO) Consortium. The developers of the Data Mining Optimization (DMOP) ontology (Hilario et al 2011), the Exposé ontology (Vanschoren et al 2012), the Ontology of Data Mining (OntoDM) (Panov et al 2008(Panov et al , 2010 and others have agreed that a DM Core resource, which provides formal standardized definitions for the principal DM entities, e.g., algorithm, data, dataset, task, implementation would be of benefit to the whole DM research community. Since then, however, there has been little progress in the development of a community agreed standard in this area.…”

Section: Introductionmentioning

confidence: 99%

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Ontology of core data mining entities

Panov

Soldatova

Džeroski

2014

Data Min Knowl Disc

Self Cite

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In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines the most essential data mining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend. OntoDM-core is available at

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Ontology of core data mining entities

Panov

Soldatova

Džeroski

2014

Data Min Knowl Disc

Self Cite

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“…Some authors (Panov et al 2010) propose heavy-weight ontology, named OntoDM, based on a general framework for data mining. It represents entities such as data, data mining tasks and algorithms, and generalisations.…”

Section: Ontologiesmentioning

confidence: 99%

On Approach for the Implementation of Data Mining to Business Process Optimisation in Commercial Companies

Pick

Vasilecas

Kalibatiene

et al. 2013

Technological and Economic Development of Economy

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Nowadays, organisations aim to automate their business processes to improve operational efficiency, reduce costs, improve the quality of customer service and reduce the probability of human error. Business process intelligence aims to apply data warehousing, data analysis and data mining techniques to process execution data, thus enabling the analysis, interpretation, and optimisation of business processes. Data mining approaches are especially effective in helping us to extract insights into customer behaviour, habits, potential needs and desires, credit associated risks, fraudulent transactions and etc. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We propose an approach for implementation of data mining into a business process. The confirmation of the suggested approach is based on the results achieved in eight commercial companies, covering different industries, such as telecommunications, banking and retail.

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“…The «Data wrangling scripts» outline in Figure 7 shows the scripts for converting data for each type of table of the «Table types» outline (ownersScript, profileScript, listScript). Just as in OntoDM [25], the relation «input of» is used to link the script program and the input data.…”

Section: Figure 7 Lightweight Vocabulary Ontology Of Data Resourcesmentioning

confidence: 99%

“…It can be seen from Table 4 which contains a generalization of the representation software tools by ontological means [21], [24][25][26] that the linking of the pairs «output data format, software» and «input data type, the algorithm» have already been implemented, which makes it possible to check the compatibility of the triple «format, data type, software». Table 4.…”

Section: Comparison Of the Resources And Railway Infrastructure Model...mentioning

confidence: 99%

Ontology-Based Semantic Checking of Data in Railway Infrastructure Information Systems

Shynkarenko

Иванов

2022

Foundations of Computing and Decision Sciences

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Semantic checking of railway infrastructure information support data is one of the ways to improve the consistency of information system data and, as a result, increase the safety of train traffic. Existing ontological developments have demonstrated the applicability of description logic for modelling railway transport, but have not paid enough attention to the data resources structure and the railway regulatory support. In this work, the formalization of the tabular presentation of data and the rules of railway transport regulations is carried out using the example of a connection track passport and temporary speed restrictions using ontological means, data wrangling and extraction tools. Ontologies of the various formats data resources and railway station infrastructure, tools for converting and extracting data have been developed. The semantic checking of the compliance of railway information system data with regulatory documents in terms of the connection track passport is carried out on the basis of a multi-level concretization model and integration of ontologies. The mechanisms for implementing the constituent ontologies and their integration are demonstrated by an example. Further research includes ontological checking of natural language normative documents of railway transport.

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Representing Entities in the OntoDM Data Mining Ontology

Cited by 11 publications

References 38 publications

Ontology of core data mining entities

Ontology of core data mining entities

On Approach for the Implementation of Data Mining to Business Process Optimisation in Commercial Companies

Ontology-Based Semantic Checking of Data in Railway Infrastructure Information Systems

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