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Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.

Xiang Z, Zheng J, Lin Y, He Y - J Biomed Semantics (2015)

Bottom Line: The OBI team used Ontorat to add assay and device terms required by ENCODE project.Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology.A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development.

View Article: PubMed Central - PubMed

Affiliation: University of Michigan, Ann Arbor, MI USA.

ABSTRACT

Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format.

Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development.

Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns.

Availability: http://ontorat.hegroup.org/.

No MeSH data available.


Ontorat software overall design and workflow. See the text for description.
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Fig2: Ontorat software overall design and workflow. See the text for description.

Mentions: We have developed the web-based Ontorat tool that implements the ontology enrichment strategy shown in Figure 1. Figure 2 lays out the Ontorat design and workflow pipeline. Specifically, on the Ontorat web page, a user enters setting options and uploads the input data file via the Ontorat web input form. The input data file is generated by populating a predesigned template file guided by the ODP as mentioned above. After accepting the input data file and setting options from the user, the web server (via a PHP script) will be able to execute two operations: 1) generation of new ontology classes with logical axioms and annotations, or 2) addition of new axioms to existing ontology terms. The Ontorat server will process the user’s requests and generate either an Ontorat settings file or an OWL output file. The Ontorat settings file can be stored and reused later. For the OWL output generation, a Manchester syntax file will be generated first and then transferred to OWL format (Figure 2).Figure 1


Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.

Xiang Z, Zheng J, Lin Y, He Y - J Biomed Semantics (2015)

Ontorat software overall design and workflow. See the text for description.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4362828&req=5

Fig2: Ontorat software overall design and workflow. See the text for description.
Mentions: We have developed the web-based Ontorat tool that implements the ontology enrichment strategy shown in Figure 1. Figure 2 lays out the Ontorat design and workflow pipeline. Specifically, on the Ontorat web page, a user enters setting options and uploads the input data file via the Ontorat web input form. The input data file is generated by populating a predesigned template file guided by the ODP as mentioned above. After accepting the input data file and setting options from the user, the web server (via a PHP script) will be able to execute two operations: 1) generation of new ontology classes with logical axioms and annotations, or 2) addition of new axioms to existing ontology terms. The Ontorat server will process the user’s requests and generate either an Ontorat settings file or an OWL output file. The Ontorat settings file can be stored and reused later. For the OWL output generation, a Manchester syntax file will be generated first and then transferred to OWL format (Figure 2).Figure 1

Bottom Line: The OBI team used Ontorat to add assay and device terms required by ENCODE project.Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology.A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development.

View Article: PubMed Central - PubMed

Affiliation: University of Michigan, Ann Arbor, MI USA.

ABSTRACT

Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format.

Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development.

Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns.

Availability: http://ontorat.hegroup.org/.

No MeSH data available.