2014 OntologyofCoreDataMiningEntitie
- (Panov et al., 2014) ⇒ Panče Panov, Larisa Soldatova, and Sašo Džeroski. (2014). “Ontology of Core Data Mining Entities.” In: Data Mining and Knowledge Discovery Journal, 28(5-6). doi:10.1007/s10618-014-0363-0
Subject Headings: OntoDM Ontology.
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- http://scholar.google.com/scholar?q=%222014%22+Ontology+of+Core+Data+Mining+Entities
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Abstract
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 http://www.ontodm.com.
1. Introduction
Intelligent information systems rely on advanced representations of application domains, tasks, methods to solve those tasks, and the available data. Ontologies as data and knowledge models offer logically defined, flexible and interoperable repre - sentations of principal domain entities to empower information systems. Ontology — the “science of being” — typically has different meanings in different contexts. In the late twentieth Century, Artificial Intelligence (AI) adopted the term in the sense of a “specification of a conceptualization” — an ontology defines a set of representational primitives to model a domain of knowledge. The primitives are typically classes (or set)s, attributes (or properties), and relations among class members (Gruber 2009).
The recent success of ontologies has been driven by the popularity of semantic web technologies. Ontologies are used to specify controlled vocabularies, which can be used to exchange data among different systems, provide services for answering queries, publish reusable knowledge bases, and offer services to facilitate interoperability across multiple, heterogeneous systems and databases (Gruber 2009). The Open Bio-Ontologies (OBO) 1 are a leading effort in ontology engineering. As biological sciences generate big and complex data, the development of bio-ontologies has been critical in handling these data and enabling interoperability between databases and between applications (Robinson and Bauer 2011).
Biomedical computing has become critically dependent on the use of ontologies. Resources such as the Gene Ontology 2 the National Cancer Institute’s Thesaurus,3 the Foundational Model of Anatomy (FMA)4, SNOMED-CT5, and the Ontology for Biomedical Investigations (OBI)6 have become integral components of modern biomedical research and practice. In the past, ontologies were perceived as arcane, over-complicated, and perhaps over-hyped. Now they serve as essential infrastructure for contemporary biology and medicine (Soldatova et al. 2010). Currently, an open repository of biomedical ontologies BioPortal7 contains over 350 ontologies. Ontologies are used to annotate experimental data, to assist information retrieval, to enable integration of heterogeneous data, to drive literature mining, and to build knowledge bases (Soldatova et al. 2010).
The OBO Foundry is coordinating the efforts on establishing a set of principles for ontology development in order to create a suite of orthogonal interoperable reference ontologies in the biomedical domain for supporting data and knowledge sharing and to avoid duplication of efforts. To ensure interoperability of bio-medical ontologies …
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References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 OntologyofCoreDataMiningEntitie | Sašo Džeroski Larisa Soldatova Panče Panov | Ontology of Core Data Mining Entities | 10.1007/s10618-014-0363-0 | 2014 |