Ask Data Anything - Election results example

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 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.

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.

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.

Ask Data Anything

Ask Data Anything is Cognitum's approach to exploring data by using a subset of natural language which articulates concepts and instances modeled in ontologies to provide a meaningful quering experience. Ask Data Anything seizes on regularities of language to provide a natural interpretation of queries being asked; its semantics are provided via R and rOntorion (alternatively  F# and Ontorion).

Technically, Ask Data Anything is capable of performing projection, sub-setting, grouping and aggregation operations, providing answers for queries involving the following information:

• What? Any of the columns of your data table are considered a quantitative field over which to perform queries,
• How? How the output is to be shown. The results of the query can be retrieved on either a table, histogram or a map,
• Where? (Optional) The "in" preposition allows to restrict the search to an specific named group of items  as happens for instance with continents which can be seeing as a group of countries,
• Of? (Optional) The "of" preposition allows to dive into the data, restricting the desired results to a certain set of types (concepts in the Fluent Editor sense) by searching the data in a certain column for instances (in Fluent Editor sense) of those types; we call this material sub-setting,
• By? (Optional) By which type (in Fluent Editor sense) you would like to group the results for aggregation purposes.
• When? (Optional) Queries can contain time constraints.

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.

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.

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.