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Frequently Asked Questions

1. What is Ontodog?

Ontodog is a web server that automatically generates ontology community views.

2. Who are primary users of Ontodog?

The users of Ontodog include bio-ontology developers, domain experts, and bioinformaticians who are using bio-ontologies for different applications. Ontodog primarily targets on ontology developers who can develop ontologies without a programming process. The Excel spreadsheets provide an convenient way for having domain experts participate in the whole ontology process.

3. What is URI?

A Uniform Resource Identifier (URI) is defined in [RFC3986] as a sequence of characters chosen from a limited subset of the repertoire of US-ASCII [ASCII] characters. URIs refer to resources.

4. Where are the source ontologies used in Ontodog stored?

Ontodog uses source ontologies stored in RDF format in Neurocommons and a RDF server in He Group in University of Michigan Medical School. The contents of source ontologies are stored in RDF triples and available for SPARQL query. Theoretically, Ontodog is able to fetch any ontology in a SPARQL queriable RDF server through internet.

5. Can we use Ontodog for ontologies developed using OBO format?

Ontodog is developed based on OWL format. Currently there are many converters that can convert OBO format to OWL format or vice versa. With the help of some converter, it is possible to use Ontodog for development of a new ontology based on OBO format.

6. Who has used Ontodog? Any successful stories?

Ontodog has been used for development of ontology community views for FGED, ....

7. How are the input and output files provided by the users stored on Ontodog servers?

The input and output files are not stored permanently on the Ontodog servers. They will be stored for up to 24 hours with a unique file name which consists of 8 random characters. These files will be automatically destroyed at 3:00 am EST (New York time) by an internal script. The temporary storage is for users to come back to the input and result files, and it also provides a way for our Ontodog developers to debug any possible errors. For those users who do have concerns on privacy and security, the users can select to destroy the input and output files immediately in the end of the Ontodog execution.

8. Is it possible to enter a favorite source ontology and SPARQL end point and then select a different ontology via the drop-down menu?

We do not allow the option of using a favorite source ontology and SPARQL endpoint and at the same time using a different ontology via the drop-down menu. Ontodog prevents a user from providing two ontology sources. Specifically, when a favorite source ontology and SPARQL endpoint are provided, the drop-down menu does not show any specific ontology. If a different ontology is selected from the drop-down menu, any text in the favorite source ontology input box will automatically be cleared.

9. Is it possible to access Ontodog programmatically?

Yes. To access Ontodog without using the Ontodog web page, try this: curl -s -F file=@/tmp/input.txt -o /tmp/output.owl http://ontodog.hegroup.org/service.php

10. What is the performance of Ontodog in ontology modularization-like process compared to other ontology modularization algorithms and programs?

Ontodog implements an Ontodog-based ontology modularization-like program. In our original Ontodog paper, the Ontodog's SPARQL method was compared with OWLAPI modularization method in terms of retrieving related ontology terms. Three sets of signature terms (individual term, small subset of terms, larger ontology file) were given as input to the Ontodog method and OWLAPI modularization method. In all three cases, both methods generated identical results. This Ontodog SPARQL-based method uses the setting "includeAllAxioms". Current modularization algorithms use in-memory representations that require excessive memory for ontologies such as NCBI Taxonomy. In contrast, the SPARQL-based approach is highly scalable.

After the Ontodog paper was published, we have made several improvements: (1) A new setting called "includeAllAxiomsRecursively" was generated. This setting allows recursive retrieval of more axioms associated with the original signature terms and those other terms retrieved afterwards. The use of this setting will likely retrieve more terms and annotations. (2) A specific Ontodog program was developed. (3) Ontodog can now retrieve instance data associated with retrieved ontology classes.

11. Do Ontodog and Ontodog use the OWLAPI modularization code?

No. Neither Ontodog nor the general Ontodog method uses the OWL API modularization code. Our programs are developed based on SPARQL and PHP coding.

12. What is an ontology axiom?

In the ontology community, axioms are used to associate class and property identifiers (IDs) with either partial or complete specifications of their characteristics, and to give other logical information about classes and properties. These used to be called definitions. However, they are not all definitions in the common sense of the term. Therefore, a more-neutral name "axiom" has been chosen. Reference: http://www.w3.org/TR/2002/WD-owl-absyn-20020729/#5.

13. How to keep updating Ontodog output results?

The Ontodog process can be executed at different times to import updated information of external ontology terms. By storing and updating the original Ontodog input text file, users can subsequently query the Ontodog server on a regular basis and get up to date information with little effort.

14. Why it did not work when I used another RDF triple store endpoint?

There might be differnt reasons. For example, we used Virtuoso RDF triple store. If you used a triple store other than Virtuoso, there might be some comparatiblity issue, which might cause the failure of Ontodog execuation or the generation of different results. Such an issue will be considered in our future Ontodog development.

See more information in Tutorial.