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.

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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.

Last comment 16 November 2017
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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.

Last comment 18 July 2017
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If you've worked with iKnow domain definitions, you know they allow you to easily define multiple data locations iKnow needs to fetch its data from when building a domain. If you've worked with DeepSee cube definitions, you'll know how they tie your cube to a source table and allow you to not just build your cube, but also synchronize it, only updating the facts that actually changed since the last time you built or synced the cube. As iKnow also supports loading from non-table data sources like files, globals and RSS feeds, the same tight synchronization link doesn't come out of the box. In this article, we'll explore two approaches for modelling DeepSee-like synchronization from table data locations using callbacks and other features of the iKnow domain definition infrastructure.

Last comment 19 April 2017
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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.

Last comment 28 March 2017
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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.

Last comment 28 March 2017
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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.

Last answer 22 December 2016 Last comment 10 January 2017
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Enterprises need to grow and manage their global computing infrastructures rapidly and efficiently while simultaneously optimizing and managing capital costs and expenses. Amazon Web Services (AWS) and Elastic Compute Cloud (EC2) computing and storage services meet the needs of the most demanding Caché based application by providing
 a highly robust global computing infrastructure.

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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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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.

Last comment 10 August 2016
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