mOntage: Building Domain Ontologies from Linked Open Data

Dastgheib, Shima; Mesbah, Arsham; Kochut, Krys J.

doi:10.1109/icsc.2013.21

Cited by 6 publications

(11 citation statements)

References 14 publications

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“…In [8,9], we have reported on a framework to extract and integrate data from heterogeneous data sources to create ProKinO, an ontology for large-scale integrative analysis of protein kinase data. Sahoo, et al described a system for creating a mash-up of gene and pathway resources and the creation of Entrez Knowledge Model which is then populated from publicly available gene and pathway resources [29].…”

Section: Related Workmentioning

confidence: 99%

“…In [8,9], we presented the mOntage framework, which allows for creating and populating domain ontologies from the data sources in the Linked Open Data cloud, which are all in the form of RDF, often available via SPARQL endpoints. In order to extend the framework to support structured data such as spreadsheets, XML, JSON and Relational Databases, we employ an RDF-Modeler, which, as its name suggests, models a variety of data formats as RDF.…”

Section: Use Case-based Conceptual Modelmentioning

confidence: 99%

“…A new domain ontology can be created by defining its classes and properties, and then specifying how to obtain instance data in terms of the concepts and properties in the existing data sources [8,9]. An ontology created in this way not only promotes the reuse of already existing concepts and properties, but also increases the interlinking of the new ontology to the existing data sources.…”

Section: Introductionmentioning

confidence: 99%

“…In [8,9], we presented mOntage, a conceptual model and a framework for creating and populating domain-specific ontologies built from the existing data sources in the 1 http://disease-ontology.org/.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Creating Biomedical Ontologies Using mOntage

Dastgheib¹,

McSkimming²,

Kannan³

et al. 2015

Lecture Notes in Computer Science

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Abstract. The growing volume of biomedical data available on the Web has contributed to numerous scientific advancements. At the same time, the complex, versatile and disparate nature of the data can overburden the knowledge discovery and data-driven hypothesis generation by scientists. Ontologies have been proposed to address the data integration challenge, however, creating useful domain-specific ontologies and populating them with high quality instances is tedious and time-consuming. In this paper, we present the mOntage framework to rapidly create ontologies representing data in a specific area of interest. We show how the mOntage framework can be used to create and populate biomedical ontologies from existing data sources. The classes and properties of the ontology being created are mapped to and instantiated from the existing data sources by executing suitable SPARQL queries. We illustrate our framework by creating a Phosphatase Ontology and show how it can serve as an important source of knowledge in the area of phosphatases.

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Section: Related Workmentioning

confidence: 99%

Section: Use Case-based Conceptual Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Creating Biomedical Ontologies Using mOntage

Dastgheib¹,

McSkimming²,

Kannan³

et al. 2015

Lecture Notes in Computer Science

Self Cite

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show abstract

“…Linking Open Government Data (LOGD) project [6] investigates translating government-related data using Semantic web technologies. LOD has gained significant adoption and momentum, though the quality of the interconnecting relationships remains questionable [7]. IBM Smarter City initiative aims at creating cities that are vital and safe for its citizens and businesses.…”

mentioning

confidence: 99%

Integrating Big Data: A Semantic Extract-Transform-Load Framework

Bansal

Kagemann

2015

Computer

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Abstract-Big Data researchers are dealing with the Variety of data that includes various formats such as structured, numeric, unstructured text data, email, video, and audio. The proposed Semantic Extract-Transform-Load (ETL) framework that uses semantic technologies to integrate and publish data from multiple sources as open linked data provides an extensible solution for effective data integration, facilitating the creation of smart urban apps for smarter living.

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Towards Ontology Refinement by Combination of Machine Learning and Attribute Exploration

Potoniec

2015

Lecture Notes in Computer Science

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mOntage: Building Domain Ontologies from Linked Open Data

Cited by 6 publications

References 14 publications

Creating Biomedical Ontologies Using mOntage

Creating Biomedical Ontologies Using mOntage

Integrating Big Data: A Semantic Extract-Transform-Load Framework

Towards Ontology Refinement by Combination of Machine Learning and Attribute Exploration

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