Ask Data Anything - NYPD Motor vehicle accidents

In modern organizations, data management is a major issue and at the same time a major resource. In our experience, the first challenge a business that wants to use its data is facing how to have a unified view of their data. Generally data inside organizations is stored in different databases that have often proprietary API making it difficult to move from one database to the other. Furthermore, also when the technology used to store data is the same, there are still semantic problems like different terminologies, languages etc.

The bigger the company is, the lower the possibility to standardize the procedures are, so that these kind of situations will not happen. This happens because we are human and we naturally tend to interpret data using our own experience and knowledge. Thus we cannot expect the technical team to call all pieces of a car using the exact same terminology as the logistic department. This is why, our solution aims at giving the possibility to standardize the way in which the end user interact with the data without actually changing the source of the data.

Ask your Data Anything (ADA), allows companies to add a semantical layer on top of the data without the need of copying data. The product is managing term disambiguation, aggregation of data using hierarchies defined in ontologies, data integration between different data sources.

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 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.

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.

Mixing Text Mining with Semantic Technologies - sample application.

The very broad subject of processing the natural language is incredibly hot nowadays. In many cases, a regular text mining approach is not adequate to the problems that we are facing. Therefore text mining methods are mixed with Natural Language Processing(NLP) methods, like also, with semantic technologies - what gives better results. One of such a problem is how to find out, if two sentences are semantically equal or not.

The solution for the above problem could be used on many fields. One of them is detection of an abusive clauses inside a contract. Sometimes it's really hard to understand correctly, the exact meaning of a clause inside a contract, even for a specialists. For a sake of presentation I have developed a simple application prototype which attempts to solve this problem. Application was developed in C# and it uses Ontorion SDK.

Input

Before running the application we need three files:

1. File with contract in which we will attempt to detect abusive clauses.
2. File with abusive clauses.
3. File with ontology.