Example of using SWRL built-ins with Solar System ontology.

Introduction to SWRL

Semantic Web Rule Language (SWRL for short) is a combination of OWL DL and OWL Lite sub-languages of OWL Web Ontology. It is possible to write ontology with SWRL built-ins in Ontorion Fluent Editor. One of such example of ontology written by using Semantic Web Rule Language is Cognitum's Solar System Ontology.

To follow along open Fluent Editor, go to File -> New and type Solar System. Double click on the template to open.

Using RDF Data Cube Vocabulary to model sales data with Fluent Editor - Example.

RDF Data Cube Vocabulary is a way to represent data in popular format with link data paradigms. Linked data is an approach to publishing data on a web and this vocabulary makes it possible. There are numerous benefits to linked data. The individual observations, and groups of observations, become (web) addressable. This allows publishers ad third parties to annotate and link to this data. For example a report can reference the specific figures it is based on allowing for fine grained provenance trace-back. Representing any data set with these benefits is now possible with Controlled Natural Language in Fluent Editor and it has never been so easy.

Medical Clinic Ontology - Example

Diseases of affluence, an aging population and many other reasons cause doctors to be overworked and tired. Many of them complain, that bureaucracy consumes a large amount of time, which could be otherwise spend on curing patients. Cognitum meets the expectation of medical workers and provides tools that can spare precious time by adding semantic layer to patients' records and doctors' medical knowledge. Cognitum's Fluent Editor can be used to quickly access patient's medical history, suggest a medicament for specific illness and even predict patient's disease based on signs and symptoms.

Using SWRL built-ins in CNL ontology

The Semantic Web Rule Language (SWRL) is an expressive OWL-based rule language. SWRL extends OWL syntax which allows users to write rules with more powerful deductive reasoning capabilities than OWL alone. SWRL built-ins are one of SWRL’s powerful features, which are predicates to be used to manipulate data values in SWRL rules.

From the latest version, Fluent Editor supports a number of core SWRL built-ins defined in SWRL Built-in Submission. In this post, we will introduce two examples of applying some of SWRL core built-ins to your CNL ontology.

SKOS and BibTeX in Creating Semantic Ontology on Medical Articles

There are numerous projects that can serve as useful foundations for forming your ontology. One of such projects is Simple Knowledge Organization System (SKOS), a W3C Recommendation.
SKOS provides a standard way to represent knowledge organization systems such as thesauri, classification schemes using the RDF. Another project is BibTeX, a method of marking up bibliographic data, primarily for use in LaTeX documents, but also useful for generic bibliographic storage.

With Fluent Editor, you can utilize both projects through importing them as references, which can be useful for expressing better organization of knowledge. In this post, we will present how SKOS and BibTeX can be utilized in creating your ontology. Data used in the following ontology is based on an excerpt from a list of medical articles on PubMed Central (PMC).

Fluent Editor 2014 R4 – SWRL Built-ins, Auto-recovery Functionality, OWL2 EL++/OWL-RL Validation, and New Features in Ontology Graph.

A new Fluent Editor 2014  R4 is now available which will present you a few new powerful features. With the new Fluent Editor you will be able to utilize core SWRL built-ins, various OWL2 profiles, visualize your ontology file more effectively, and more to benefit while editing and exploring ontology files. Such new feather are as follows :

• SWRL built-ins
• Auto-recovery functionality
• OWL2-EL++ / OWL-RL validation
• New features in ontology diagram
• Various performance improvements

Using Active Rules on Fluent Editor

Active rules is the mechanism Fluent Editor provides for executing custom imperative code in C# upon meeting certain criteria described as SWRL rules. Considering that standard SWRL rule axioms consist of two parts, an antecedent (body) and consequent (head), the body part of active rules exactly follows the standard format of SWRL rules. The difference from standard SWRL lies on the head part, on which you can add your custom C# code as a set of execution commands. We provide some mechanism to deal with the nature of distributed environments through a set of core built-ins. Available functions to use in active rules are as follows:
 

• KnowledgeInsert(string knowledge) : Inserts knowledge into your ontology.
• KnowledgeDelete(string knowledge) : Deletes knowledge into your ontology.
• WriteMessage(string msg) : Prints msg on Active Rules Output window. 

Visualization of Ontology Contents - with Ontology Diagram and SWRL Debugger

From the latest release version, Fluent Editor includes a few new features that enhance visualization of ontology, which can help you navigate through the structure of your ontology more intuitively. Possibility of drawing a diagram of ontology is one of such features. Ontology diagrams graphically express relations between concepts and instances - also the materialized ones. 
Another new feature to be introduced is SWRL debugger. SWRL debugger shows binding of rules that took place during the reasoning process over your ontology. It is presented as a list of all the SWRL rules and bound instances. By running SWRL debugger, you can check how SWRL rules work on your ontology.

In this post, we will look through these two features further.

Using OWL Annotation in Fluent Editor

OWL Annotations together with SKOS and DcTerms form a widely used Thesaurus standard that help the ontology modeler to give meaningful names to elements of the ontology or to relates elements in various ontology. In the latest release of Fluent Editor, we have introduced the possibility to add, remove and modify OWL annotations with full support for SKOS and DcTerms. As always this has been implemented thinking of the usability over everything. 

All actions related to the annotations are reachable from the Annotation tab that was added in the right column of the Fluent Editor window. To see how to use annotations in Fluent Editor,you can open the Book Reference template. To see the template, click on File -> New  and then Book Reference.

Collaborative ontology editing with the use of Fluent Editor and the Ontorion Server

In the latest release of Fluent Editor, we have implemented a simple and intuitive way for multiple users to edit the same ontology at the same time. This is possible by using the functionalities of both Fluent Editor and Cognitum's scalable knowledge management system Ontorion. In this article we will try to give you a general understanding of how this concurrent editing of ontologies is working.

As a comment we would like to stress that the component that we will show you has been implemented in C# using the Ontorion API (that is part of the Ontorion Server). If thus have access to the Ontorion API and Ontorion Server, you can implement all functionalities that you see in this article in your custom program. For more information about Ontorion Server and the Ontorion API you can contact us here.

First of all open Fluent Editor, click File, Open&Import , Ontorion Server and then Connect to Ontorion.

Reasoning about ontologies - fast vs. complete answers

In this article you will gain more intuition about: - how to query your ontology - the difference between reasoner and materialized graph - what is materialization mode OWL-DL and materialization mode OWL-RL+ - when you can use faster OWL RL+ reasoning mode safely You will see two example ontologies: - about books (using data types, cardinality restriction, data type restrictions) - about political preferences (SWRL rules, defining concepts by enumeration) You can reproduce the steps by downloading the ontologies: - my_books.encnl - political_parties.encnl and opening it with FluentEditor on your computer.

About reasoners and materialized graph

With recent FluentEditor releases the user has three tools to query the ontology:

• reasoner of choice (in this example Hermit reasoner is used)
• materialized graph (we can use either OWL-DL or OWL-RL+ materialization mode)
• SPARQL queries

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.

Using the rOntorion package in R / RStudio and Fluent Editor

The rOntorion package is the port of Cognitum's Semantic Technologies to R. R has become an important tool among Statisticians and Data Scientists and we are proud to provide this community with an enhanced Linked-Data manipulation experience that will allow them to edit, store and reason over structured data (in the supported formats ocnl, rdf and owl); henceforth discovering new horizons in Data Analysis. rOntorion allows to extend Fluent Editor in R and in turn provides the users with the capability of creating their own custom functionality.

rOntorion in R

To demonstrate the use of rOntorion directly from R, let us go through a minimal example. In this example we are going to reason over a set of dummy sentences and infer a single logical conclusion by querying the semantic engine with a question expressed in ocnl format. First we need to install rOntorion: to do so, issue the following command in an R Console:

Fluent Editor 2014 R1 available

So here it is: Fluent Editor 2014 R1

You may have noticed that Cognitum recently released a new version of Fluent Editor, a comprehensive ontology editor for OWL and SWRL.

The idea is still the same, but the software has been significantly rebuilt. Thanks to the great feedback from the community and dozens of ontologies both developed with Fluent Editor and reused/"consumed" with it, new version has many features improved to make it easier and more straightforward to work with existing ontologies and create new ones from scratch.

It’s all about productivity and simplicity while creating, editing and consuming ontologies.

New UI

New Fluent Editor cames with new User Interface that make user more familiar with Microsoft Office. The New UI allows for fast opening several ontologies at once, share them easily and navigate through them.

Ontorion SPARQL Tools for Excel

We have just released a SPARQL addin for Excel. This Addin has been developed by Cognitum as part of its semantic technology framework: Ontorion Server and Fluent Editor. Ontorion SPARQL Tools for Excel is a free Addin to MS Excel 2010 and 2013 and can be downloaded here. This addin has been designed to help people in the Knowledge Management business or people interested in the data available from SPARQL endpoints to easily import data from a SPARQL endpoint and/or an Ontorion Server. With the number of SPARQL endpoint growing year after year, this seems as a very important feature that was lacking in Excel until the present day. As you will see with our adding it is possible to do all of this with few single clicks.

 

Oncology App - Clinical Decision Support System

Hi, this is Ola form Cognitum. Today I will show you yet another problem which can be solved efficiently by CNL semantic technology.

The problem

Nowadays the medical knowledge is greater than ever. There are official medical guidelines that every doctor is obliged to follow. There are plenty of study cases and a lot of statistical data which help evaluate the probability of successful treatment. Every patient should have right to take advantage of most recent medical research. However, it is hard to do on daily basis. How can the doctor analyze all the books with medical data during patient's visit?

Our solution

Our Oncology Application guides the doctor throughout the patient’s visit. It suggests next steps of the therapy in accordance with up to date medical standards.
When the physician enters the essential information into the application, the system analyzes the clinical situation and tries to generate recommendations for the given condition. The recommendations are in accordance with European Society for Medical Oncology standards. It is the doctor who is still the most important in the decision making process but now he is aided with a professional tool analyzing the wealth of in-depth knowledge amassed in the field of medical oncology.