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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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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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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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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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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