Monday, 2 February 2015

Fluent Editor's Interoperability with Protégé

Protégé is a great tool for editing ontologies allowing deep insight into the structure of the OWL ontology. Fluent Editor allows user to focus on actual meaning of the ontology (taxonomy, vocabulary, rule set, etc) being edited.
From the R2 release, Fluent Editor enables you to view and build ontology with both applications synchronously, through which you can enjoy those great features of both applications at the same time. This is supported by two related functionalities. -  exporting ontology from one window to the other, or importing ontology from the opened window to your current window. In this post we will look through how you can utilize this feature.


Initial Settings
By default, this interoperability with Protégé is disabled. In order to enable it, first you need to edit settings of the Protégé plug-in on Tab > Options as shown below. Set "Yes" for enabling the plug-in and enter your Protégé path on the bottom.


Exporting/Importing Ontology 
To export your opened CNL ontology from Fluent Editor to Protégé, simply click External Tab>Export to Protege.

If Protégé is not running, Fluent Editor will run Protégé first and send a command for exporting. If the exporting has been successfully done, Fluend Editor will show you this popup message.

And you can see the Protégé window loaded with the ontology which you exported from Fluent Editor.

Once exporting is performed successfully, it means that a connection is established between two windows with a unique connection ID. From then on, these two windows will communicate with each other for the further import/export requests.

Exporting/Importing ontologies on Protégé side works in the same way as it does on Fluent Editor.


Example:

Here we present an example of how the functionality works in detail, with a simple IT infrastructure ontology.

First, create a sample IT ontology...

As you see below, we have some comments here - those comments are not managed by Protégé.

Moreover, we have some modal expressions here, they are not even part of OWL, it is our own extension that allows to write requirements for the knowledge itself. You will see below that these elements can be kept throughout the Protégé inter-operation, even if they are not compatible with Protégé. 

Now, let's export this ontology to Protégé.

And then, on Protégé, we make some changes on this ontology. 
For example, we can add some annotations for Server concept....

....like this.

We can also add something more. Let's add a new instance, such as Server3 as below.

Now let's export this updated ontology back to Fluent Editor... 

... by overwriting the coordinating Fluent Editor window.  

After exporting is successfully done, let's take a look at the Fluent Editor window. You can find that :
  • The entire formatting is preserved. See all comments are in their place? 
  • The newly created instance is added at the end of file. (Server-3)

  • Moreover, we have new annotations here. You can see it using annotation window as below.




If you want to learn more about Fluent Editor 2014 R2, visit this link.



*) FluentEditor 2014 R2, ontology editor, is a comprehensive tool for editing and manipulating complex ontologies that uses Controlled Natural Language. Fluent editor provides one with a more suitable for human users alternative to XML-based OWL editors. It's main feature is the usage of Controlled English as a knowledge modeling language. Supported via Predictive Editor, it prohibits user from entering any sentence that is grammatically or morphologically incorrect and actively helps the user during sentence writing. 

1 comment:

  1. The development of artificial intelligence (AI) has propelled more programming architects, information scientists, and different experts to investigate the plausibility of a vocation in machine learning. Notwithstanding, a few newcomers will in general spotlight a lot on hypothesis and insufficient on commonsense application. machine learning projects for final year In case you will succeed, you have to begin building machine learning projects in the near future.

    Projects assist you with improving your applied ML skills rapidly while allowing you to investigate an intriguing point. Furthermore, you can include projects into your portfolio, making it simpler to get a vocation, discover cool profession openings, and Final Year Project Centers in Chennai even arrange a more significant compensation.


    Data analytics is the study of dissecting crude data so as to make decisions about that data. Data analytics advances and procedures are generally utilized in business ventures to empower associations to settle on progressively Python Training in Chennai educated business choices. In the present worldwide commercial center, it isn't sufficient to assemble data and do the math; you should realize how to apply that data to genuine situations such that will affect conduct. In the program you will initially gain proficiency with the specialized skills, including R and Python dialects most usually utilized in data analytics programming and usage; Python Training in Chennai at that point center around the commonsense application, in view of genuine business issues in a scope of industry segments, for example, wellbeing, promoting and account.

    ReplyDelete