Ontology of core data mining entities

Panov, Panče; Soldatova, Larisa; Džeroski, Sašo

doi:10.1007/s10618-014-0363-0

Cited by 47 publications

(31 citation statements)

References 57 publications

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“…The Ontology of Core Data Mining Entities (OntoDM) [26] represents data mining tasks, generalizations, data mining algorithms, and more. The pattern presented in Table 8 describes the common features of subclasses of a class that only has a numeric identifier in the ontology, but no textual label.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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The research goal of this work is to investigate modeling patterns that recur in ontologies. Such patterns may originate from certain design solutions, and they may possibly indicate emerging ontology design patterns. We describe our tree-mining method for identifying the emerging design patterns. The method works in two steps: (1) we transform the ontology axioms in a tree shape in order to find axiom patterns; and then, (2) we use association analysis to mine co-occuring axiom patterns in order to extract emerging design patterns. We conduct an experimental study on a set of 331 ontologies from the BioPortal repository. We show that recurring axiom patterns appear across all individual ontologies, as well as across the whole set. In individual ontologies, we find frequent and non-trivial patterns with and without variables. Some of the former patterns have more than 300,000 occurrences. The longest pattern without a variable discovered from the whole ontology set has size 12, and it appears in 14 ontologies. To the best of our knowledge, this is the first method for automatic discovery of emerging design patterns in ontologies. Finally, we demonstrate that we are able to automatically detect patterns, for which we have manually confirmed that they are fragments of ontology design patterns described in the literature. Since our method is not specific to particular ontologies, we conclude that we should be able to discover new, emerging design patterns for arbitrary ontology sets.

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Section: Experiments and Resultsmentioning

confidence: 99%

Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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“…The advocation of an ontology based approach for the specification of PHPDM models has been due to the ability of ontologies to provide an axiomatic foundation of the specification language and the realisation of corresponding reasoning processes that can aid decision making. The specific ontology that we have introduced for specifying PHPDM models has been based on three sub-ontologies, namely: (i) an ontology for governmental policy making (i.e., the G2G Ontology [13]); (ii) an ontology for data mining (i.e., the OntoDM-core ontology [14]); and (iii) an ontology for statistics (i.e., the STATO ontology [15]). …”

Section: Specification Of Phpdm Modelsmentioning

confidence: 99%

“…As discussed above, we use the OntoDM-core ontology [14], to specify in a formal manner the key data mining processes that should be applied to data in order to produce evidence. For this purpose, we have extended the OntoDM-core ontology by introducing classes to representing the different data mining algorithms of WEKA [16].…”

Section: Specification Of Phpdm Modelsmentioning

confidence: 99%

Towards a Model-Driven Platform for Evidence based Public Health Policy Making

Prasinos

Spanoudakis

Koutsouris

2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-The effective management of various health conditions depends on and requires appropriate public health policies (PHP). Such policies are important for several aspects of healthcare provision, including: (a) screening for prevention of disease; (b) early diagnosis and treatment; (c) long-term management of chronic diseases and disabilities; and (d) setting-up standards. Although it is widely recognised that the PHP life cycle (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, current support for it is mainly in the form of guidelines, and is not supported by data analytics and decision making tools tailored to it. In this paper, we present a novel model driven approach to PHP life cycle management and an integrated platform for realising this life cycle. Our approach is based on PHP decision making models. Such models steer the PHP decision making process by defining the data that need to be collected and the ways in which these data should be analysed in order to produce the evidence required for PHP making. Our work is part of a new research programme on public health policy making for the management of hearing loss, called EVOTION, that is funded by the European Union.

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“…While there is no universally established data mining ontology yet, there are several data mining ontologies currently under development, such as the Knowledge Discovery (KD) Ontology [140], the KDDONTO Ontology [141], the Data Mining Workflow (DMWF) Ontology 28 [142], the Data Mining Optimization (DMOP) Ontology 29 by Hilario [143,144], OntoDM 30 [145,146], and its sub ontology modules OntoDT, 31 OntoDM-core 32 [147] and OntoDM-KDD 33 [148]. An overview of existing intelligent assistants for data analysis that use ontologies is given in [149].…”

Section: Domain-independent Approachesmentioning

confidence: 99%

“…To allow the representation of mining structured data, the authors developed a separate ontology module, named OntoDT, for representing the knowledge about datatypes. To represent core data mining entities, and to be general enough to represent the mining of structured data, the authors introduced the second ontology module called OntoDM-core [147]. The third, and final, module of the ontology is the OntoDM-KDD which is used for representing data mining investigations [148].…”

Section: Domain-independent Approachesmentioning

confidence: 99%

Semantic Web in data mining and knowledge discovery: A comprehensive survey

Ristoski

Paulheim

2016

Journal of Web Semantics

244

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Ontology of core data mining entities

Cited by 47 publications

References 57 publications

Discovery of emerging design patterns in ontologies using tree mining

Discovery of emerging design patterns in ontologies using tree mining

Towards a Model-Driven Platform for Evidence based Public Health Policy Making

Semantic Web in data mining and knowledge discovery: A comprehensive survey

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