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#iKnow

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InterSystems iKnow is unique technology for text exploration that enables you to gain insight from unstructured data and use it to enrich your solutions. iKnow is different because it uses a “bottom up” approach to text exploration, discovering concepts and relations within the text itself. With iKnow technology, there is no need for the tedious – and limiting – process of predefining dictionaries or ontologies.

See the related iKnow videos.

 

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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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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I am trying to create an iKnow domain programmatically like:

    Set dom = ##class(%iKnow.Domain).%New("TestDom")
    Do  dom.SetParameter("DefaultConfig", "MyConfiguration")
    Set sc = dom.%Save()

   ...

Although "MyConfiguration" sets the language to "ja", i.e. Japanese, it doesn't seem to be respected, and what I see on the top right pane in Knowledge Portal is related concepts, instead of proximity profiles, which I expect to see in Japanese language mode.

Also resulting segmentation of sentences looks to be it is in English mode.

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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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I have iKnow domain of forum posts, their full text is an iKnow data, and each post also has a number of views as a metadata field.

I want to get a sum of views by concept. Let's say I have a concept called "TESTEST" and there are 10 sources that have this concept. Each source has some views. I want to get views total - impact of this concept so to say.

What's the best iKnow architecture for this use case?

So far I got this:

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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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Hello!

We use iKnow's GetSimilar for decision making. Right now we have a domain with both good and bad documents and using GetSimilar we want to see if a document is more similar to the good ones or the bad ones. To do this we simply compare the weighted average of the score from the good ones and the bad ones that GetSimilar returns.

The problem is that GetSimilar doesn't always return the score to all other documents. Assuming we have 50 documents I would expect the following result:

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Question
· Mar 29, 2016
iKnow Architect - metadata

Hello,

I am experimenting with the iKnow Domain Architect in the 2016.2 field test.

I would like to know if it is possible to load metadata from text files. This was possible in previous versions, using the Loading Wizard.

I have checked the documentation and I do not see any explanation for loading metadata held in text files. Thank you!

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I have a class which, in the previous instance, was able to extract metadata field names and data from a text file, and load this information into a domain. I am trying to run this in the field test instance, but it is not loading the metadata - only the field names. I am not getting an error, but the data is not loaded.

The few changes I made to the original class:

Previously, this class also ran iTables. I commented all that code out.

To create the domain, I replaced the line:

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Presenter: Danny Wijnschenk
Task: Help people make better decisions by letting application deal with all the data.
Approach: As an example, we’ll extend a demo asset management application for portfolio and trade compliance, using iKnow technology to translate agreements into rules that ensure portfolio compliance prior to trade execution.

In this session, we’ll discuss how easy it is to extend a classic application that deals with straightforward transactions, to also offer insights and actions based on more complex, unstructured data. We’ll present a use case on portfolio compliance from the financial services industry.

Content related to this session, including slides, video and additional learning content can be found here.

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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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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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Hi-

I have a SQL Query using %iFind.Highlight which returns text highlighting certain words and phrases. %iFind.Highlight seems to remove cr/lf from the returned text.

Here's my query

ClassMethod Search(pSessionId As %String, pSearchString As %String) As %String
{
set tTags="<span style='background-color:yellow;'>"
&sql(
SELECT %iFind.Highlight(Text , :pSearchString , , :tTags) into :results
FROM SSA_OCR.TempSearchable where sessionId = :pSessionId)
quit results
}

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Hi Community,

Enjoy watching the new video on InterSystems Developers YouTube:

⏯ iKnow: Open Source NLP in InterSystems IRIS

https://www.youtube.com/embed/n-TL_Wh7Tv4
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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 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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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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Hi Community!

New video "Treating Patients with REST and iKnow" is available now on DC YouTube Channel:

https://www.youtube.com/embed/HDTij5GS_qQ
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Presenter: Misha Bouzinier
Task: Gain an understanding of natural language processing and the current state of the art
Approach: Discuss how InterSystems iKnow technology fits into the NLP ecosystem and complements the output of other components such as Lucene and Stanford NLP tools

A 101 session on Natural Language Processing that positions Intersystems tools in the broader ecosystem Problem: we’ve been touting “unstructured data” for five years, but many people both internally and externally still don’t know what it means to “process natural language” in general and how iKnow and our upcoming UIMA capabilities fit in this NLP ecosystem. This session will describe what a number of common technologies offer and how bare-bone NLP output typically needs to be complemented with more classic analytics or inference tooling to get the value out.

Content related to this session, including slides, video and additional learning content can be found here.

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Hi Community!

Enjoy the video of the week about InterSystems iKnow Technology:

A Cure for Clinician Frustration

https://www.youtube.com/embed/johncrm-O0E
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Presenter: Dirk Van Hyfte
Task: Leverage unstructured data to improve how clinicians deliver care
Approach: Give real-world examples of organizations that are benefiting from using their unstructured data

This session will feature real-world examples of how healthcare organizations can benefit from exposing unstructured data to clinicians at point-of-care as well as to clinical informatics building predictive models. Presenters are Wesley Williams, PhD, Vice President and Chief Information Officer, Mental Health Center of Denver; Augie Turano PhD. IT Director Veterans Informatics and Computer Infrastructure (VINCI); and Dirk Van Hyfte, MD, PhD, Senior Research Consultant.

Content related to this session, including slides, video and additional learning content can be found here.

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