#InterSystems Natural Language Processing (NLP, iKnow)

InterSystems' iKnow technology allows you to identify the concepts in natural language texts and the relations that link them together. As that's still a fairly abstract definition, we produced this video to explain what that means in more detail. But when meeting with customers, what really counts is a compelling demonstration, on data that makes sense to them, so they understand the value in identifying these concepts over classic top-down approaches. That's why it's probably worth spending a few articles on some of the demo apps and tools we've built to work with iKnow.

In the first article in this series, we'll start with the Knowledge Portal, a simple query interface to explore the contents of your domain.

After a five-part series on sample iKnow applications (parts 1, 2, 3, 4, 5), let's turn to a new feature coming up in 2017.1: the iKnow REST APIs, allowing you to develop rich web and mobile applications. Where iKnow's core COS APIs already had 1:1 projections in SQL and SOAP, we're now making them available through a RESTful service as well, in which we're trying to offer more functionality and richer results with fewer buttons and less method calls. This article will take you through the API in detail, explaining the basic principles we used when defining them and exploring the most important ones to get started.

Earlier in this series, we've presented four different demo applications for iKnow, illustrating how its unique bottom-up approach allows users to explore the concepts and context of their unstructured data and then leverage these insights to implement real-world use cases. We started small and simple with core exploration through the Knowledge Portal, then organized our records according to content with the Set Analysis Demo, organized our domain knowledge using the Dictionary Builder Demo and finally build complex rules to extract nontrivial patterns from text with the Rules Builder Demo.

This time, we'll dive into a different area of the iKnow feature set: iFind. Where iKnow's core APIs are all about exploration and leveraging those results programmatically in applications and analytics, iFind is focused specifically on search scenarios in a pure SQL context. We'll be presenting a simple search portal implemented in Zen that showcases iFind's main features.

This is the third article in a series on iKnow demo applications, showcasing how the concepts and context provided through iKnow's unique bottom-up approach can be used to implement relevant use cases and help users be more productive in their daily tasks. Previous articles discussed the Knowledge Portal, a straightforward tool to browse iKnow indexing results, and the Set Analysis Demo, in which you can use the output of iKnow indexing to organize your texts according to their content, such as in patient cohort selection.

This week, we'll look into another demo application, the Dictionary Builder demo, in which we'll marry iKnow's bottom-up insights with top-down expertise, organizing our domain knowledge into dictionaries that are composed of the actual terms used in the data itself. Sticking to a top-down approach only, you'd risk missing out on some terminology used in the field that a domain expert sitting in his office wouldn't be aware of.

In previous articles on iKnow, we described a number of demo applications (iKnow demo apps parts 1, 2, 3, 4 & 5) that are either part of the regular kit or can be easily installed from GitHub. All of those applications assumed you already had your iKnow domain ready, with your data of interest loaded and ready for exploration. In this article, we'll shed more light on how exactly you can get to that stage: how you define and then build a domain.

Last week at the InterSystems BeNeLux Symposium, we announced the publication of the InterSystems iKnow Natural Language Processing technology to Open Source. This enables developers and researchers to take advantage of this unique technology at any level of their application stack, workflow or experiment.

This article contains the tutorial document for a Global Summit academy session on Text Categorization and provides a helpful starting point to learn about Text Categorization and how iKnow can help you to implement Text Categorization models. This document was originally prepared by Kerry Kirkham and Max Vershinin and should work based on the sample data provided in the SAMPLES namespace.

In my cache studio i couldn't find the a namespace of iknow so how can i check is my studio version is compatible to to the one i am using now. If i don't have one then can be able to create a new namespace in studio?

A simple and rather automated search portal leveraging iFind capabilities for rich text search in 2016.1. It has simple faceting, result ranking, highlighting of search results etc and just works off any table you point it to that has an iFind index by appending ?t=MyPackage.TableName to the URL.

See also https://github.com/bdeboe/isc-iknow-ifindportal for more details and the latest version.

Hello!

I'm trying to use iKnow but I get the following error "No iKnow-enabled namespaces found" in the Management Portal.

It was working on the 2015.2.2 version but now on 2016.2.0 it doesn't. I very new to Caché and iKnow so it's probably a simple problem.

I was able to create a iKnow domain using the terminal and I can view it in the learning portal (http://localhost:57772/csp/sys/exp/_iKnow.UI.IndexingResults.zen).

Hello!

I'm trying to make a simple hello world program for iKnow but the documentation only shows examples using the scripting language or XML XData.

The domain is already created and configured, what I hope for is to be able to work with the data in some high-level language.

Is it possible to do something like this:

int domainId = 1;
IKnow iknow = new IKnow( domainId );
List concepts = iknow.getTopConcepts();

Any feedback will be greatly appreciated!

Hi,

I try to implement an iFind index.

Here is my definition class :

Has anyone built tag cloud from iKnow domain?

If so, any advice on how to do that?