InterSystems Official
· Feb 18, 2020
iKnow goes Open Source!

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.

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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.

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Article
· Jul 4, 2016 8m read
Introduction to the iKnow REST APIs

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.

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The InterSystems DBMS has a built-in technology for working with non-structured data called iKnow and a full-text search technology called iFind. We decided to take a dive into both and make something useful. As the result, we have DocSearch — a web application for searching in InterSystems documentation using iKnow and iFind.

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This is the second 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. Last week's article discussed the Knowledge Portal, a straightforward tool to browse iKnow indexing results.

This week, we'll look into the Set Analysis demo, a slightly more advanced application where you'll be using the concepts identified by iKnow to organize your content into sets of documents. The original version of this demo was developed by Danny Wijnschenk & Alain Houf for an academy session at GS2015, but the app has evolved significantly since then.

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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.

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Sentiment Analysis is a thriving research area in the broader context of big data, with many small as well as large vendors offering solutions extracting sentiment scores from free text. As sentiment is highly dependent on the subject a piece of text is about (financial news vs tweets about the latest computer game), most of these solutions are targeted at specific markets and/or focus on a given type of source data, such as social media content.

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This is the fourth 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, the Set Analysis Demo and the Dictionary Builder Demo, each of which gradually implemented slightly more advanced interactions with what iKnow gleans from unstructured data.

This week, we'll look into one more demo application, the Rules Builder Demo, in which we'll build on previous work but again climb a step on the level ladder, implementing a more high-level use case than in the previous ones. The idea came from an opportunity where we were asked to help the customer in the finance sector make sense of vast volumes of contract data. They wanted to semi-automate the extraction of logical rules from that text (in fluent legalese!), so they could be fed into other systems. While this was an exciting use case to work on (and more on it in this GS2016 presentation), we've also used it in other cases, for example to extract mentions of ejection fraction from Electronic Health Records.

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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.

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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.

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This earlier article already announced the new iKnow REST APIs that are included in the 2017.1 release, but since then we've added extensive documentation for those APIs through the OpenAPI Specification (aka Swagger), which you'll find in the current 2017.1 release candidate. Without wanting to repeat much detail on how the APIs are organised, this article will show you how you can consult that elaborate documentation easily with Swagger-UI, an open source utility that reads OpenAPI specs and uses it to generate a very helpful GUI on top of your API.

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InterSystems has decided to stop further development of the InterSystems IRIS Natural Language Processing, formerly known as iKnow, technology and label it as deprecated as of the 2023.3 release of InterSystems IRIS. InterSystems will continue to support existing customers using the technology, but does not recommend starting new development projects outside of the core text exploration use cases it was originally designed for.

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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.

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I'm in a process of acquiring a corpus of documents on educational courses.

For example there is an educational course called "OOP" and it can have documents from 2008, 2009, ... 2016 etc.
And there are a lot of these courses, each one with programs from different years (hopefully)

So 1 document is 1 programm of one course for one year.

I want to calculate how much does a course changes per year.

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This summer the Database Platforms department here at InterSystems tried out a new approach to our internship program. We hired 10 bright students from some of the top colleges in the US and gave them the autonomy to create their own projects which would show off some of the new features of the InterSystems IRIS Data Platform. The team consisting of Ruchi Asthana, Nathaniel Brennan, and Zhe “Lily” Wang used this opportunity to develop a smart review analysis engine, which they named Lumière. As they explain:

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Question
· Feb 26, 2016
No Namespaces found

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).

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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!

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A group of students at the Chalmers University of Technology (Gothenburg, Sweden) tried different approaches to automatically rating the quality of emergency calls, including iKnow.

Excerpt: "The most impressive results produced by iKnow is its ability to correctly classify 100% of the calls using the Average algorithm. This is quite surprising since iKnow only compares low-level concepts, how words relates to each other."

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Article
· Nov 9, 2015 1m read
iFind Search Portal

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.

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